Neural Plasticity

Neural Plasticity / 2018 / Article

Research Article | Open Access

Volume 2018 |Article ID 3678534 |

Emiliano Santarnecchi, Chiara Del Bianco, Isabella Sicilia, Davide Momi, Giorgio Di Lorenzo, Salvatore Ferrone, Giulia Sprugnoli, Simone Rossi, Alessandro Rossi, "Age of Insomnia Onset Correlates with a Reversal of Default Mode Network and Supplementary Motor Cortex Connectivity", Neural Plasticity, vol. 2018, Article ID 3678534, 10 pages, 2018.

Age of Insomnia Onset Correlates with a Reversal of Default Mode Network and Supplementary Motor Cortex Connectivity

Academic Editor: Stuart C. Mangel
Received17 Jul 2017
Revised14 Feb 2018
Accepted06 Mar 2018
Published01 Apr 2018


Insomnia might occur as result of increased cognitive and physiological arousal caused by acute or long acting stressors and associated cognitive rumination. This might lead to alterations in brain connectivity patterns as those captured by functional connectivity fMRI analysis, leading to potential insight about primary insomnia (PI) pathophysiology as well as the impact of long-term exposure to sleep deprivation. We investigated changes of voxel-wise connectivity patterns in a sample of 17 drug-naïve PI patients and 17 age-gender matched healthy controls, as well as the relationship between brain connectivity and age of onset, illness duration, and severity. Results showed a significant increase in resting-state functional connectivity of the bilateral visual cortex in PI patients, associated with decreased connectivity between the visual cortex and bilateral temporal pole. Regression with clinical scores originally unveiled a pattern of increased local connectivity as measured by intrinsic connectivity contrast (ICC), specifically resembling the default mode network (DMN). Additionally, age of onset was found to be correlated with the connectivity of supplementary motor area (SMA), and the strength of DMN←→SMA connectivity was significantly correlated with both age of onset (R2 = 41%) and disease duration (R2 = 21%). Chronic sleep deprivation, but most importantly early insomnia onset, seems to have a significant disruptive effect over the physiological negative correlation between DMN and SMA, a well-known fMRI marker of attention performance in humans. This suggests the need for more in-depth investigations on the prevention and treatment of connectivity changes and associated cognitive and psychological deficits in PI patients.

1. Introduction

Primary insomnia (PI) is a clinical condition characterized by troubles initiating or maintaining sleep, which is associated with daytime consequences and is not attributable to environmental circumstances or inadequate opportunity to sleep, as well as not to any other somatic or psychiatric cause [1]. In the last decades, PI has become more prevalent in industrialized nations (estimated to affect between 5% and 10% of the general population) [2, 3] and is associated with detrimental effects on cognition [4] as well as quality of life [5, 6], work productivity [7], and work-related injuries [8], as well as with increased vulnerability to general medical disorders [6, 9], psychiatric ones in particular [10, 11]. Recent neuroimaging studies have shed light on the potential neuroanatomical and functional correlates of PI, with structural magnetic resonance imaging (MRI) investigations showing abnormal grey matter volume in multiple brain regions, such as the hippocampus [12], medial frontal lobes [13], parietal cortex [14], and anterior cingulate cortex [15]. In addition to structural alteration, functional MRI (fMRI) studies have shown a variety of modifications induced by sleep deprivation or poor sleep quality, in both healthy participants [16] and patients with sleep disorders [1720]. A variety of techniques and a priori hypotheses have been tested and validated, showing alterations of fMRI activity in regions related to attention and memory processing, as well as regions of the default mode network (DMN). However, these investigations, based on a priori selection of regions of interest (ROIs) capturing the activity of a given region/network [21], or based on an arbitrary anatomo-functional parcellation of the brain [22], might lead to a partial view of the possible insomnia-related alteration of brain activity, induced by sampling the activity of a limited subset of cortical and subcortical regions fitting with a given theory or pathophysiological model.

Moreover, recent evidence has suggested that insomnia might have an impact on both night and day brain functioning, with changes in brain plasticity, assessed via transcranial magnetic stimulation (TMS), reported in patients with PI [23]. Such a broad repercussion on central nervous system dynamics might also suggest an interaction between age-related brain plasticity mechanisms [24], length of exposure to sleep deprivation, and age of insomnia onset. Insight about how these factors might affect brain connectivity patterns, not available to date, might also help in defining novel therapeutic interventions and targets for insomnia.

Therefore, we investigated differences in resting-state functional connectivity fMRI patterns in drug-naïve patients with PI compared to healthy controls, looking at correlations between insomnia-induced alterations and clinical variable, in particular age of onset and disease duration. Importantly, in order to avoid a priori selection of analysis ROIs/masks, we implemented a high-resolution FC analysis based on voxel-wise connectivity maps indexing both local and distributed functional connectivity patterns for each voxel in the brain. We hypothesized that PI patients will display altered connectivity in sensory systems and/or regions related to attention and memory processing. We also hypothesized that insomnia duration and age of onset might exert similar effects on brain functional connectivity patterns, with early onset possibly leading to a stronger disruption of physiological brain dynamics.

2. Materials and Methods

2.1. Participants

Seventeen drug-naïve insomnia patients and 17 age- and education-matched healthy controls participated in the study. Diagnosis was based on the ICSD-3 criteria for primary insomnia (PI). All the participants were right-handed (as measured using the Oldfield handedness scale), cognitively intact (Mini-Mental State Examination score > 24), and monolingual native speakers and underwent a general physical and neurological screening, as well as an assessment of their medical history. PI patients were diagnosed and enrolled at the Center for Sleep Medicine of the Le Scotte Hospital (Siena, Italy). Each patient completed self-report clinical scales assessing the severity of their sleep-related complaints (Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Scale (ISI)) and their mood status (Beck Depression Inventory (BDI)). Inclusion criteria for patients were as follows: (1) fist diagnosis of primary insomnia at our center; (2) 18 to 45 years old; (3) no evidence of other medical disorders, with particular reference to current or past neurological and psychiatric ones or other sleep disorders; (4) no history of assumption of drugs acting on the central nervous system; and (5) no previous treatment or diagnosis of primary insomnia. They were advised to drink no more than one cup of coffee (or two of tea) and to not assume any amount of alcohol or other type of drink with caffeine in the day of the radiological examination. Healthy controls showed a normal neurological exam, regular sleep-wake cycle and no sleep complaints. Exclusion criteria were as follows: (1) abnormalities in physical and neurological examination screening visit, (2) current or past substance abuse, (3) use of psychotropic medication within 3 months prior to inclusion, and (4) brain structural abnormalities at the magnetic resonance imaging (MRI) exam. All participants gave their written informed consent to the experimental procedure, which conformed to the Declaration of Helsinki. The study was approved by the local ethical committee.

2.2. Clinical Assessment

Patients came to the Center for Sleep Medicine reporting sleep-related complaints involving “difficulty falling asleep or staying asleep, waking up early in the morning, and/or poor sleep quality with daytime consequences.” They were diagnosed for the first time by two neurologists (CDB and IS) licensed as Sleep Disorders Expert by the Italian Society for Sleep Medicine (Associazione Italiana Medicina del Sonno (AIMS); The clinical evaluation included a clinical interview, assessment of clinical symptoms, evaluation of sleep diary, review of current and past medical and medication history, and clinical scales designed for depression (Beck Depression Inventory (BDI)), sleep disorders (Pittsburgh Sleep Quality Index (PSQI)), and insomnia in particular (Insomnia Severity Index). Patients had never assumed any drug treatment for insomnia, even if 3 patients have been trying herbal supplements until two months before undergoing the neuroradiological evaluation.

The Pittsburgh Sleep Quality Index (PSQI) [25] is a 19-item retrospective self-report questionnaire designed to provide a reliable, standardized measure of sleep quality and discriminate “good” and “poor” sleepers. Specifically, seven clinically derived domains are assessed (i.e., sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medications, and daytime dysfunction) composing a general score referring to global sleep quality. A score higher than 5 is considered an indicator of relevant sleep disturbances. A recently validated Italian version of the PSQI with high internal consistency was used [26].

The Insomnia Severity Index (ISI) is a brief self-report measure assessing perception of the severity of sleep disturbance [27]. Focusing on the past 1 month period, ISI evaluates the severity of sleep onset, sleep maintenance and early morning awakening problems, sleep dissatisfaction, interference of sleep difficulties with daytime functioning, noticeability of sleep problems by others, and distress caused by sleep difficulties. A 5-point Likert scale is used to rate each item (e.g., 0 = no complaint and 4 = severe complaint), with a total score ranging from 0 to 28. Higher scores indicate more severe insomnia, according to the following classifications: (i) absence of insomnia (0–7); (ii) subthreshold insomnia (8–14); (iii) clinical insomnia (moderate severity, 15–21); and (iv) clinical insomnia (severe, 22–28).

The Beck Depression Inventory (BDI) [28, 29] is a 21-item self-assessment questionnaire measuring the severity of symptoms and attitudes related to depression. It consists of 21 statements describing the somatic and cognitive-emotional symptoms of depression. Each item consists of four alternative responses graded from 0 to 3 according to the severity of the symptom. Patients are asked to choose the response better representing their mood state during the last 7 days. The total BDI score ranges from 0 to 63, with mild mood disturbance and clinical depression corresponding to, respectively, a score of 10 and 21 (or above).

2.3. MRI Data Acquisition

MRI data was acquired on a Philips Intera whole-body scanner. Resting-state fMRI data included 178 volumes with 33 axial slices covering the whole brain, acquired via a T2 BOLD-sensitive multislice echo planar imaging (EPI) sequence (TR/TE = 2.5 s/32 ms; field of view = 22 cm; image matrix =  64 × 64; voxel size =  3.44 × 3.44 × 3.8 mm3; flip angle = 75°). Structural imaging was performed using a whole brain T1-weighted Fast Field Echo 1 mm3 sequence (TR/TE = 30/4.6 ms, field of view = 250 mm, matrix 256 × 256, flip angle = 30°, slice number = 150, and scan time: 7 : 25 minutes). T2-weighted fluid-attenuated inverse recovery (FLAIR) images were also acquired to assess participants’ white matter integrity. Participants were provided with earplugs and were instructed to lay in the scanner with their eyes open, while fixating on a cross hair. They were asked to stay as still as possible. To monitor the patients’ state inside the scanner, the MRI technician monitored each patient via a camera placed inside the scanner for the entire fMRI acquisition. Particular care was taken to minimize head motion via vacuum cushions and custom-made padding.

2.4. fMRI Preprocessing

fMRI data preprocessing and statistical analyses were carried out using SPM8 software (Statistical Parametric Mapping;, FSL for brain extraction procedure using the BET script (, and MATLAB 7.5 (MathWorks, MA, USA). The first three volumes of functional images were discarded for each subject to allow for steady-state magnetization. EPI images were slice-time corrected using the interleaved descending acquisition criteria and realigned and resliced using a mean functional volume derived from the overall fMRI scans. Subjects whose head motion exceeded 1.0 mm or rotation exceeded 1.0° during scanning were excluded. In order to obtain the better estimation of brain tissues maps, we implemented an optimized segmentation and normalization process using DARTEL (Diffeomorphic Anatomical Registration using Exponential Lie Algebra) [30] module for SPM8. Briefly, this approach is based on the creation of a customized anatomical template built directly from participants’ T1-weighted images instead of the canonical one provided with SPM (MNI template, ICBM 152, Montreal Neurological Institute). This allows for a finer normalization into standard space and consequently avoids under- or overestimation of brain region volume possibly induced by the adoption of an external template. Hidden Markov Random Field model was applied in all segmentation processes in order to remove isolated voxels. Customized tissue prior images and T1-weighted template were smoothed using an 8 mm full-width at half-maximum (FWHM) isotropic Gaussian kernel. Functional images were consequently nonlinearly normalized to standard space, and a voxel resampling to isotropic 3 × 3 × 3 mm were applied. Linear trends were removed to reduce the influence of the rising temperature of the MRI scanner, and all functional volumes were band-pass filtered at (0.01 Hz < f < 0.08 Hz) to reduce the low-frequency drift. Finally, the CompCor algorithm has been applied in order to control physiological high-frequency respiratory and cardiac noise [31].

2.5. Intrinsic Connectivity Contrast

Individual connectivity maps were computed by means of the intrinsic connectivity contrast (ICC), a voxel-to-brain connectivity metric [32]. Differently from other local connectivity indexes, ICC takes into account not only the presence of a connection but also their strength, thereby producing voxel-based connectivity maps without the need for defining any ROIs. This index also has the advantage that it can be computed without applying a correlation threshold, and therefore, it does not require any a priori information or assumptions. ICC was applied according to the following formula:

ICC is computed for each voxel in the brain, therefore producing a whole-brain map where the intensity of each voxel reflects the average R2 connectivity of a given voxel i and all the other voxels in the brain. For statistical purposes, ICC values were normalized to fit a Gaussian distribution with zero mean and unitary variance by subtracting the ICC obtained at each voxel by the average value across all the voxels and dividing this by the standard deviation of the whole-brain map [33]. Resulting ICC maps have a spatial resolution of 3 mm3.

2.6. Functional Connectivity (FC) Analysis

Resting state FC analysis was implemented using ad hoc scripts implemented in a Python and MATLAB computational environment, based on code and modules from the same software used for preprocessing of MRI data. Analysis was based on voxel-wise connectivity indexes using the intrinsic connectivity contrast (ICC) [32] (see dedicated paragraph). To avoid any a priori hypothesis about specific brain regions or networks being involved in PI pathophysiology or correlated with symptoms, FC analysis was performed using a two-step procedure. (i) First, data were analyzed by comparing voxel-wise connectivity maps at the highest possible spatial resolution (3 mm3), looking for differences in resting-state (RS) brain activity at the single-voxel level. This provides a set of significant regions whose RS connectivity patterns are different (i.e., increased or decreased) between patients and controls, with this pattern representing either the activity of an isolated cluster of voxels with no clear anatomo-functional correspondence or actually matching the spatial topography of well-known resting-state networks [34]. This ensured that any result was not due to a priori selection of analysis masks or inflated by the reduction of statistical comparisons. However, even though significant clusters represent a spatially unconstrained information about “how” BOLD fMRI activity is different across groups or in relation to a given variable (e.g., age), they do not specify whether, for instance, the connectivity profile of cluster A (e.g., located in the right temporal lobe) is different in patients and controls because of its decreased connectivity with a specific other region of the brain, or multiple others, or an entire hemisphere, and so on. To derive such information, (ii) significant clusters were then introduced to a seed-based connectivity analysis investigating the pattern of connectivity between each significant voxel-level cluster and the rest of the brain. This two-step procedure allowed to obtain (i) unconstrained high-resolution targets not referring to any existing anatomo-functional atlas and (ii) a profile of their differences in connectivity as compared to healthy controls. Interestingly, according to the unconstrained nature of first-level analysis, the emergence of patterns of significant voxels resembling one or more known networks should be interpreted as a stronger indication of their relevance, given that no anatomical constraint was applied.

2.7. Statistical Analysis
2.7.1. Group Differences

Voxel-wise connectivity maps were compared across PI patients and HC, using an analysis of covariance (ANCOVA) including age, gender, and BDI score as covariates. Results were considered significant at a threshold equal to , with false discovery rate (FDR) correction. As specified above, significant clusters were then used as seed regions in a second-level seed-based connectivity analysis. The same statistical thresholds were applied in both analyses.

2.7.2. Correlation with Clinical Scores

The same approach was used to derive patterns of disease-related modifications in patients’ connectivity profile. Analysis were run only in PI patients (). Voxel-wise regression models were built by predicting age of onset, disease duration, and ISI scores (FDR, ; FWE, ), followed by seed-based analysis using resulting significant clusters.

3. Results

3.1. Clinical Assessment

The selected PI patients () reported an average age of onset of 28.82 ± 10.15 yrs, with an average score at the PSQI of 15 ± 2.35 and an ISI of 18.45 ± 5.16. They also did not report a significant deflection in mood levels (BDI = 8 ± 4.52) and no clinically significant cognitive decline (MMSE = 28 ± 2). Healthy controls also did not report any mood-related symptomatology (BDI = 6 ± 3.86; group comparison: , ) and an intact cognitive profile (MMSE = 29 ± 1; group comparison: , ).

3.2. Voxel-Wise Connectivity Group Differences

Analysis of voxel-wise connectivity maps lead to significant differences in ICC patterns. Increase in connectivity of the bilateral occipital lobe was observed in PI patients with respect to controls (Figure 1(a)). Subsequent seed-based analysis highlighted a pattern of increased connectivity between bilateral occipital lobe and superior occipital lobe (i.e., increased local connectivity), as well as a decrease in connectivity with bilateral temporal pole structures (Figure 1(b)). Statistical results and anatomical localization of each cluster are reported in Table 1.

ProcedureCluster MNI coordinateskCluster localizationCluster p-FDRIncreased/decreased connectivity

ICC—voxel wise14−9042328973 voxels, primary visual cortex (left)0.00005
1076 voxels, primary visual cortex (right)
341 voxels, lingual gyrus (right)
132 voxels, lingual gyrus (left)
Seed-based38−66122893477 voxels, brain stem0.00003
153 voxels, frontal orbital cortex (right)
139 voxels, hippocampus (right)
132 voxels, temporal pole (right)
111 voxels, lateral occipital cortex, inferior division (right)
−42−4801702117 voxels, middle temporal gyrus, posterior division (left)0.0002
112 voxels, amygdala (left)
92 voxels, hippocampus (left)
80 voxels, parahippocampal gyrus, anterior division (left)
51 voxels, middle temporal gyrus, temporooccipital part (left)
0−8610590156 voxels, intracalcarine cortex (left)0.008556
96 voxels, intracalcarine cortex (right)

3.3. Correlation with Clinical Scores

The regression model predicting age of onset highlighted a pattern of increased ICC in multiple clusters of voxels resembling the default mode network (DMN; Figures 2(a) and 2(b); Table 2). In particular, seed-based analysis unveiled a significant correlation between the DMN cluster shown in (a) and (b) and the bilateral supplementary motor area (SMA; Figure 2(c)), a brain region that is negatively correlated with the DMN in healthy controls (Figure 2(d)). No significant patterns were observed for insomnia severity (), whereas close to significance results were obtained for disease duration (). Statistical results and anatomical localization are reported in Table 2. Correlation between the strength of DMN-SMA connectivity and clinical scores are displayed in Figure 2(e), accounting for 21% and 41% of variance in insomnia duration and age of onset, respectively.

ProcedureCluster MNI coordinateskCluster localizationCluster p-FDRIncreased/decreased connectivity

ICC—voxel wise838285609759 voxels, paracingulate gyrus (right)0.00003
758 voxels, superior frontal gyrus (right)
480 voxels, superior frontal gyrus (left)
456 voxels, paracingulate gyrus (left)
439 voxels, frontal pole (left)
261 voxels, middle frontal gyrus (right)
255 voxels, cingulate gyrus, anterior division
243 voxels, frontal medial cortex
125 voxels, middle frontal gyrus (left)
8−563851032996 voxels, precuneus cortex0.00004
1249 voxels, cingulate gyrus, posterior division
122 voxels, cuneal cortex (right)
107 voxels, intracalcarine cortex (right)
44−682616021220 voxels, lateral occipital cortex, superior division (right)0.000006
303 voxels, angular gyrus (right)
−44−622412641000 voxels, lateral occipital cortex, superior division (left)0.00007
228 voxels, angular gyrus (left)
Seed-based2165636711790 voxels, supplementary motor area (left)0.0002
1540 voxels, supplementary motor area (right)

4. Discussion

Data showed how chronic insomnia is able to induce changes in brain connectivity, with a specific impact on visual cortex resting-state activity. Moreover, individual differences in age of onset and insomnia duration were identified as a predictor of changes of connectivity patterns between the DMN and a core region of the motor system (SMA). Interestingly, age of onset displayed a significantly stronger correlation with fMRI alteration than disease duration, suggesting the importance of addressing insomnia-related effects on brain connectivity in younger adults to prevent long-lasting connectivity reshaping.

4.1. Insomnia-Induced Changes in FC

The most prominent difference in voxel-wise FC between PI patients and healthy controls was evident in the bilateral visual cortex. Interestingly, this finding has not been reported in any previous fMRI study on PI, whereas it fits with prior evidence of abnormal FC pattern within the occipital cortex in sleep-deprived healthy subjects [21]. Also, Morgan and colleagues reported a selectively increased occipital γ-aminobutyric acid (GABA) level in PI patients as compared to healthy controls [35]. A similar result, but extended to the entire brain, was reported by Nofzinger and colleagues, showing a greater global cerebral glucose metabolism during NON-REM sleep and wakefulness in patients with insomnia [36]. The occipital hyperactivation observed in PI patients fits with the hyperarousal theory of insomnia [37], positing a hypersensibility to external stimuli which might be driven by an overactivity of visual brain regions in patients. Increased connectivity within visual, and other sensory regions, may contribute to sustained sensory processing of environmental stimuli, ultimately hampering the ability to initiate or maintain sleep [38]. This also fits with a recent evidence of increased connectivity between the insula and the salience networks in patients with PI [39], given the role of regions of the salience network in, among other functions, monitoring bodily sensation and attribute salience to external and proprioceptive stimuli.

In addition, seed-based analysis highlighted a reduction of connectivity between the occipital lobe and two clusters mapping on the bilateral temporal pole, in particular with the hippocampus. While modifications of temporal lobe activity have been reported in PI patients [12], to our knowledge, this specific occipito-temporal connectivity change is novel. The impact of sleep deprivation on temporal lobe structures is widely documented in both humans and animal models, with disruption of memory consolidation processes [40] and local connectivity patterns [41]. Interestingly, human anatomo-functional data reported the connection between occipital and temporal cortex as part of a network involved in processing of visual stimuli [42, 43], a process possibly facilitated by the presence of a direct white matter fiber bundle connecting the hippocampal region and the visual cortex (inferior longitudinal fasciculus) [44]. Our results might point to changes in consolidation of visually encoded information, suggesting that the hyperactivity of visual brain regions in PI patients could cause the observed occipito-temporal functional “disconnection,” presumably having a “protective” effect on temporal pole function. Future investigations are needed to understand whether such alteration has a specific clinical meaning or just represents a wider and less specific set of connectivity changes resulting from chronic sleep deprivation.

4.2. Correlation with Insomnia Duration and Age of Onset

A significant correlation between individual connectivity patterns and both insomnia duration and age of onset was also found. We highlighted a very interesting correlation, yet preliminary and limited by sample size, between the age of onset and the strength of connectivity of regions highly resembling the DMN (i.e., medial prefrontal cortex, precuneus, and bilateral angular gyrus). Interestingly, using resting-state fMRI in healthy controls under controlled sleep deprivation, two studies have demonstrated an aberrant functional activity both within the DMN and between the DMN and its negatively correlated regions [16, 45]. On the other hand, greater sleep time the night before the fMRI scan seems to correlate with increased RS connectivity between two nodes of the DMN (medial prefrontal cortex and posterior cingulate cortex) in healthy volunteers, as well as with more negative correlations between these regions and those parts of negatively correlated resting-state networks (lateral prefrontal regions, parietal attention, and occipital sensory cortices) [46]. Furthermore, a longitudinal structural MRI study has also recently documented a structural disconnection between anterior and posterior regions of the DMN in patients with PI compared to healthy controls [47]. All these results point to an insomnia-related alteration of DMN activity, a network known for his role in memory processing as well as in attention-related processes when its negative correlation with other networks is considered [48, 49]. Interestingly, seed-based analysis originally highlighted the source of DMN’s activity alteration in an increased positive connectivity with bilateral SMA. Specifically, patients with early age of onset (i.e., around 20–25 years old) display a reversal of resting-state DMN-SMA connectivity patterns, that is, a positive connectivity instead of the widely reported negative correlation between DMN fMRI oscillatory activity and that of the rest of the brain [48]. The involvement of a motor system region as the SMA in PI patients might be surprising, but evidence involving motor system alterations in both patients and healthy controls have been reported. For instance, a recent EEG study demonstrated significantly elevated spectral power values in the EEG beta frequency band during NREM stage 2 in PI patients, an EEG feature mainly viewed as a general index of cortical arousal in sleep [50]. Even more interestingly, a recent study has documented the specific role played by DMN-SMA connectivity during a vigilance/attention task in healthy controls [51]. The authors reported how better individual performance at the task performed in the MRI scanner specifically reflects the strength of the negative correlation between DMN and SMA. The presence of a strong negative correlation corresponded to shorter reaction times and better overall performance, while a reversal of such dynamic, captured by looking at second-by-second BOLD fMRI activity, leads to a general worsening of attention. The reduced DMN-SMA negative correlation observed in our sample might represent one of the neurofunctional substrates of patients’ attention deficits and should be explored with ad hoc experimental designs.

Age of onset-related remapping of brain functional architecture might be related to plasticity mechanisms, which seems to change across the lifespan [24] and to be amplified in the younger brain [52]. A recent report has suggested that PI patients have altered use-dependent plasticity (UDP), one of the mechanisms underlying formation of motor memory traces and considered a sensitive measure to assess neuroplasticity in the motor system as well as a proxy of brain plasticity in general. By using transcranial magnetic stimulation (TMS), the authors found that insomnia patients display increased UDP changes relative to controls, also showing enhanced intracortical facilitation (i.e., an index of glutamatergic mechanism) relative to controls, in the absence of changes in intracortical inhibitory (GABAergic mechanism) measures [23]. Overall, patients seemed to show a heightened state of neuroplasticity possibly due to altered glutamatergic circuits and reflecting a form of maladaptive plasticity. A similar mechanism might be responsible for the observed modulation of functional connectivity depending on age of onset, suggesting the need for longitudinal TMS-based assessment of cortical plasticity in patients across the lifespan and age-of-onset distribution.

If replicated in independent samples, the reversal of DMN-SMA dynamics highlighted in PI patients will suggest the need of early interventions aimed at counteracting such disruption of resting-state brain connectivity patterns, possibly using noninvasive brain stimulation (NIBS) [53, 54] techniques. The use of NIBS in sleep disorders has not been extensively explored [55], with the recent technical evidence of the possibility of targeting specific fMRI networks, instead of single brain regions, possibly representing an intuitive approach to engage the DMN and preserve its physiological negative correlation. Moreover, insomnia has a high comorbidity with depression, and the two conditions share some of the neurobiological markers, making the quest for identifying stimulation targets even more important. Most importantly, PI is no longer considered a secondary condition to depression but rather an independent clinical entity; while insomnia is a risk factor for depression onset [56, 57], depression treatments are not a sufficient remedy for insomnia [58]. Even further, it seems that insomnia-targeted cognitive behavioral therapy (CBT) might be a better therapeutic approach to cure both insomnia and depression than CBT based on depression symptoms: a randomized trial comparing the efficacy of CBT for insomnia and depression (tested in separate groups of patients with both diagnoses) have shown insomnia treatment inducing more beneficial effects than depression treatment, in both conditions [59]. Combining CBT and network-based brain electrical stimulation might be an option to be considered. Also, the need for a better understanding of insomnia pathophysiology and possible restorative options is even more important when considered in the context of neurodegenerative disorders, with the recently documented impact of sleep-deprivation on amyloid clearance [60], the link between sleep-wake cycle and amyloid dynamics [61], as well as the general association between sleep deprivation and age of Alzheimer’s disease onset.

4.3. Conclusion

The present findings suggest the importance of exploring the role of brain plasticity mechanism into compensating for early insomnia onset and prolonged exposure to sleep deprivation. Functional data also suggest a significant enhancement of resting-state activity in the visual cortex of PI patients, corroborating the hyperarousal theory of insomnia and possibly representing a target for therapeutic interventions.

Conflicts of Interest

All authors report no conflict of interest.


  1. M. J. Sateia, “International classification of sleep disorders-third edition,” Chest, vol. 146, no. 5, pp. 1387–1394, 2014. View at: Publisher Site | Google Scholar
  2. M. M. Ohayon, “Epidemiology of insomnia: what we know and what we still need to learn,” Sleep Medicine Reviews, vol. 6, no. 2, pp. 97–111, 2002. View at: Publisher Site | Google Scholar
  3. M. M. Ohayon and C. F. Reynolds, “Epidemiological and clinical relevance of insomnia diagnosis algorithms according to the DSM-IV and the international classification of sleep disorders (ICSD),” Sleep Medicine, vol. 10, no. 9, pp. 952–960, 2009. View at: Publisher Site | Google Scholar
  4. C. Ilioudi, P. Martín-Plasencia, J. Fernández-Mendoza, S. Olavarrieta-Bernardino, and A. Vela-Bueno, “Deficiency of executive functions in chronic primary insomnia,” Neuroscience Letters, vol. 500, pp. e36–e37, 2011. View at: Publisher Site | Google Scholar
  5. G. K. Zammit, J. Weiner, N. Damato, G. P. Sillup, and C. A. McMillan, “Quality of life in people with insomnia,” Sleep, vol. 22, Supplement 2, pp. S379–S385, 1999. View at: Google Scholar
  6. D. Léger, C. Guilleminault, G. Bader, E. Lévy, and M. Paillard, “Medical and socio-professional impact of insomnia,” Sleep, vol. 25, no. 6, pp. 621–625, 2002. View at: Publisher Site | Google Scholar
  7. K. Sarsour, A. Kalsekar, R. Swindle, K. Foley, and J. K. Walsh, “The association between insomnia severity and healthcare and productivity costs in a health plan sample,” Sleep, vol. 34, no. 4, pp. 443–450, 2011. View at: Publisher Site | Google Scholar
  8. R. C. Kessler, P. A. Berglund, C. Coulouvrat et al., “Insomnia, comorbidity, and risk of injury among insured Americans: results from the America insomnia survey,” Sleep, vol. 35, no. 6, pp. 825–834, 2012. View at: Publisher Site | Google Scholar
  9. K.-L. Chien, P.-C. Chen, H.-C. Hsu et al., “Habitual sleep duration and insomnia and the risk of cardiovascular events and all-cause death: report from a community-based cohort,” Sleep, vol. 33, no. 2, pp. 177–184, 2010. View at: Publisher Site | Google Scholar
  10. M. L. Perlis, L. J. Smith, J. M. Lyness et al., “Insomnia as a risk factor for onset of depression in the elderly,” Behavioral Sleep Medicine, vol. 4, no. 2, pp. 104–113, 2006. View at: Publisher Site | Google Scholar
  11. K. M. Wright, T. W. Britt, P. D. Bliese, A. B. Adler, D. Picchioni, and D. Moore, “Insomnia as predictor versus outcome of PTSD and depression among Iraq combat veterans,” Journal of Clinical Psychology, vol. 67, no. 12, pp. 1240–1258, 2011. View at: Publisher Site | Google Scholar
  12. D. Riemann, U. Voderholzer, K. Spiegelhalder et al., “Chronic insomnia and MRI-measured hippocampal volumes: a pilot study,” Sleep, vol. 30, no. 8, pp. 955–958, 2007. View at: Publisher Site | Google Scholar
  13. B. A. Mander, V. Rao, B. Lu et al., “Prefrontal atrophy, disrupted NREM slow waves and impaired hippocampal-dependent memory in aging,” Nature Neuroscience, vol. 16, no. 3, pp. 357–364, 2013. View at: Publisher Site | Google Scholar
  14. C. E. Sexton, A. B. Storsve, K. B. Walhovd, H. Johansen-Berg, and A. M. Fjell, “Poor sleep quality is associated with increased cortical atrophy in community-dwelling adults,” Neurology, vol. 83, no. 11, pp. 967–973, 2014. View at: Publisher Site | Google Scholar
  15. J. W. Winkelman, D. T. Plante, L. Schoerning et al., “Increased rostral anterior cingulate cortex volume in chronic primary insomnia,” Sleep, vol. 36, no. 7, pp. 991–998, 2013. View at: Publisher Site | Google Scholar
  16. J. A. De Havas, S. Parimal, C. S. Soon, and M. W. L. Chee, “Sleep deprivation reduces default mode network connectivity and anti-correlation during rest and task performance,” NeuroImage, vol. 59, no. 2, pp. 1745–1751, 2012. View at: Publisher Site | Google Scholar
  17. E. Altena, Y. D. Van Der Werf, E. J. Sanz-Arigita et al., “Prefrontal hypoactivation and recovery in insomnia,” Sleep, vol. 31, no. 9, pp. 1271–1276, 2008. View at: Publisher Site | Google Scholar
  18. D. Stoffers, E. Altena, Y. D. van der Werf et al., “The caudate: a key node in the neuronal network imbalance of insomnia?” Brain, vol. 137, no. 2, pp. 610–620, 2014. View at: Publisher Site | Google Scholar
  19. S. P. A. Drummond, M. Walker, E. Almklov, M. Campos, D. E. Anderson, and L. D. Straus, “Neural correlates of working memory performance in primary insomnia,” Sleep, vol. 36, no. 9, pp. 1307–1316, 2013. View at: Publisher Site | Google Scholar
  20. E. Santarnecchi, I. Sicilia, J. Richiardi et al., “Altered cortical and subcortical local coherence in obstructive sleep apnea: a functional magnetic resonance imaging study,” Journal of Sleep Research, vol. 22, no. 3, pp. 337–347, 2012. View at: Publisher Site | Google Scholar
  21. W. D. S. Killgore, Z. J. Schwab, M. Kipman, S. R. Deldonno, and M. Weber, “Insomnia-related complaints correlate with functional connectivity between sensory–motor regions,” NeuroReport, vol. 24, no. 5, pp. 233–240, 2013. View at: Publisher Site | Google Scholar
  22. R. Pang, Y. Zhan, Y. Zhang et al., “Aberrant functional connectivity architecture in participants with chronic insomnia disorder accompanying cognitive dysfunction: a whole-brain, data-driven analysis,” Frontiers in Neuroscience, vol. 11, p. 259, 2017. View at: Publisher Site | Google Scholar
  23. R. E. Salas, J. M. Galea, A. A. Gamaldo et al., “Increased use-dependent plasticity in chronic insomnia,” Sleep, vol. 37, no. 3, pp. 535–544, 2014. View at: Publisher Site | Google Scholar
  24. C. Freitas, F. Farzan, and A. Pascual-Leone, “Assessing brain plasticity across the lifespan with transcranial magnetic stimulation: why, how, and what is the ultimate goal?” Frontiers in Neuroscience, vol. 7, p. 42, 2013. View at: Publisher Site | Google Scholar
  25. D. J. Buysse, C. F. Reynolds, T. H. Monk, S. R. Berman, and D. J. Kupfer, “The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research,” Psychiatry Research, vol. 28, no. 2, pp. 193–213, 1989. View at: Publisher Site | Google Scholar
  26. G. Curcio, D. Tempesta, S. Scarlata et al., “Validity of the Italian version of the Pittsburgh Sleep Quality Index (PSQI),” Neurological Sciences, vol. 34, no. 4, pp. 511–519, 2013. View at: Publisher Site | Google Scholar
  27. C. H. Bastien, A. Vallières, and C. M. Morin, “Validation of the Insomnia Severity Index as an outcome measure for insomnia research,” Sleep Medicine, vol. 2, no. 4, pp. 297–307, 2001. View at: Publisher Site | Google Scholar
  28. A. T. Beck, C. H. Ward, M. Mendelson, J. Mock, and J. Erbaugh, “An inventory for measuring depression,” Archives of General Psychiatry, vol. 4, no. 6, pp. 561–571, 1961. View at: Publisher Site | Google Scholar
  29. A. T. Beck, Depression: Causes and Treatment, University of Pennsylvania Press, Philadelphia, PA, USA, 2009.
  30. J. Ashburner, “A fast diffeomorphic image registration algorithm,” NeuroImage, vol. 38, no. 1, pp. 95–113, 2007. View at: Publisher Site | Google Scholar
  31. Y. Behzadi, K. Restom, J. Liau, and T. T. Liu, “A component based noise correction method (CompCor) for BOLD and perfusion based fMRI,” NeuroImage, vol. 37, no. 1, pp. 90–101, 2007. View at: Publisher Site | Google Scholar
  32. R. Martuzzi, R. Ramani, M. Qiu, X. Shen, X. Papademetris, and R. T. Constable, “A whole-brain voxel based measure of intrinsic connectivity contrast reveals local changes in tissue connectivity with anesthetic without a priori assumptions on thresholds or regions of interest,” NeuroImage, vol. 58, no. 4, pp. 1044–1050, 2011. View at: Publisher Site | Google Scholar
  33. R. L. Buckner, J. Sepulcre, T. Talukdar et al., “Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease,” Journal of Neuroscience, vol. 29, no. 6, pp. 1860–1873, 2009. View at: Publisher Site | Google Scholar
  34. O. Sporns, “Network attributes for segregation and integration in the human brain,” Current Opinion in Neurobiology, vol. 23, no. 2, pp. 162–171, 2013. View at: Publisher Site | Google Scholar
  35. P. T. Morgan, E. F. Pace-Schott, G. F. Mason et al., “Cortical GABA levels in primary insomnia,” Sleep, vol. 35, no. 6, pp. 807–814, 2012. View at: Publisher Site | Google Scholar
  36. E. A. Nofzinger, D. J. Buysse, A. Germain, J. C. Price, J. M. Miewald, and D. J. Kupfer, “Functional neuroimaging evidence for hyperarousal in insomnia,” The American Journal of Psychiatry, vol. 161, no. 11, pp. 2126–2128, 2004. View at: Publisher Site | Google Scholar
  37. M. L. Perlis, D. E. Giles, W. B. Mendelson, R. R. Bootzin, and J. K. Wyatt, “Psychophysiological insomnia: the behavioural model and a neurocognitive perspective,” Journal of Sleep Research, vol. 6, no. 3, pp. 179–188, 1997. View at: Publisher Site | Google Scholar
  38. D. Riemann, K. Spiegelhalder, B. Feige et al., “The hyperarousal model of insomnia: a review of the concept and its evidence,” Sleep Medicine Reviews, vol. 14, no. 1, pp. 19–31, 2010. View at: Publisher Site | Google Scholar
  39. M. C. Chen, C. Chang, G. H. Glover, and I. H. Gotlib, “Increased insula coactivation with salience networks in insomnia,” Biological Psychology, vol. 97, pp. 1–8, 2014. View at: Publisher Site | Google Scholar
  40. T.-M. Prince and T. Abel, “The impact of sleep loss on hippocampal function,” Learning & Memory, vol. 20, no. 10, pp. 558–569, 2013. View at: Publisher Site | Google Scholar
  41. R. Havekes, A. J. Park, J. C. Tudor et al., “Sleep deprivation causes memory deficits by negatively impacting neuronal connectivity in hippocampal area CA1,” eLife, vol. 5, article e13424, 2016. View at: Publisher Site | Google Scholar
  42. J. R. Vidal, M. Perrone-Bertolotti, J. Levy et al., “Neural repetition suppression in ventral occipito-temporal cortex occurs during conscious and unconscious processing of frequent stimuli,” NeuroImage, vol. 95, pp. 129–135, 2014. View at: Publisher Site | Google Scholar
  43. O. Jensen, B. Gips, T. O. Bergmann, and M. Bonnefond, “Temporal coding organized by coupled alpha and gamma oscillations prioritize visual processing,” Trends in Neurosciences, vol. 37, no. 7, pp. 357–369, 2014. View at: Publisher Site | Google Scholar
  44. M. Catani, “Occipito-temporal connections in the human brain,” Brain, vol. 126, no. 9, pp. 2093–2107, 2003. View at: Publisher Site | Google Scholar
  45. I. M. Verweij, N. Romeijn, D. J. Smit, G. Piantoni, E. J. W. van Someren, and Y. D. van der Werf, “Sleep deprivation leads to a loss of functional connectivity in frontal brain regions,” BMC Neuroscience, vol. 15, no. 1, p. 88, 2014. View at: Publisher Site | Google Scholar
  46. S. Suh, H. Kim, T. T. Dang-Vu, E. Joo, and C. Shin, “Cortical thinning and altered cortico-cortical structural covariance of the default mode network in patients with persistent insomnia symptoms,” Sleep, vol. 39, no. 1, pp. 161–171, 2016. View at: Publisher Site | Google Scholar
  47. M. D. Fox, A. Z. Snyder, J. L. Vincent, M. Corbetta, D. C. Van Essen, and M. E. Raichle, “The human brain is intrinsically organized into dynamic, anticorrelated functional networks,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 27, pp. 9673–9678, 2005. View at: Publisher Site | Google Scholar
  48. R. N. Spreng, W. D. Stevens, J. P. Chamberlain, A. W. Gilmore, and D. L. Schacter, “Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition,” NeuroImage, vol. 53, no. 1, pp. 303–317, 2010. View at: Publisher Site | Google Scholar
  49. K. Spiegelhalder, W. Regen, B. Feige et al., “Increased EEG sigma and beta power during NREM sleep in primary insomnia,” Biological Psychology, vol. 91, no. 3, pp. 329–333, 2012. View at: Publisher Site | Google Scholar
  50. O. Hinds, T. W. Thompson, S. Ghosh et al., “Roles of default-mode network and supplementary motor area in human vigilance performance: evidence from real-time fMRI,” Journal of Neurophysiology, vol. 109, no. 5, pp. 1250–1258, 2013. View at: Publisher Site | Google Scholar
  51. V. Anderson, M. Spencer-Smith, and A. Wood, “Do children really recover better? Neurobehavioural plasticity after early brain insult,” Brain, vol. 134, no. 8, pp. 2197–2221, 2011. View at: Publisher Site | Google Scholar
  52. E. Santarnecchi, A. K. Brem, E. Levenbaum, T. Thompson, R. C. Kadosh, and A. Pascual-Leone, “Enhancing cognition using transcranial electrical stimulation,” Current Opinion in Behavioural Sciences, vol. 4, pp. 171–178, 2015. View at: Publisher Site | Google Scholar
  53. D. B. Fisher, P. Fried, G. Ruffini et al., “Network-targeted non-invasive brain stimulation with multifocal tdcs,” Brain Stimulation, vol. 10, no. 2, pp. 411-412, 2017. View at: Publisher Site | Google Scholar
  54. L. Frase, H. Piosczyk, S. Zittel et al., “Modulation of total sleep time by transcranial direct current stimulation (tDCS),” Neuropsychopharmacology, vol. 41, no. 10, pp. 2577–2586, 2016. View at: Publisher Site | Google Scholar
  55. C. Baglioni, G. Battagliese, B. Feige et al., “Insomnia as a predictor of depression: a meta-analytic evaluation of longitudinal epidemiological studies,” Journal of Affective Disorders, vol. 135, no. 1–3, pp. 10–19, 2011. View at: Publisher Site | Google Scholar
  56. D. E. Ford and D. B. Kamerow, “Epidemiologic study of sleep disturbances and psychiatric disorders. An opportunity for prevention?” JAMA, vol. 262, no. 11, pp. 1479–1484, 1989. View at: Publisher Site | Google Scholar
  57. C. E. Carney, Z. V. Segal, J. D. Edinger, and A. D. Krystal, “A comparison of rates of residual insomnia symptoms following pharmacotherapy or cognitive-behavioral therapy for major depressive disorder,” The Journal of Clinical Psychiatry, vol. 68, no. 2, pp. 254–260, 2007. View at: Publisher Site | Google Scholar
  58. K. Blom, S. Jernelöv, C. Rück, N. Lindefors, and V. Kaldo, “Three-year follow-up comparing cognitive behavioral therapy for depression to cognitive behavioral therapy for insomnia, for patients with both diagnoses,” Sleep, vol. 40, no. 8, 2017. View at: Publisher Site | Google Scholar
  59. L. Xie, H. Kang, Q. Xu et al., “Sleep drives metabolite clearance from the adult brain,” Science, vol. 342, no. 6156, pp. 373–377, 2013. View at: Publisher Site | Google Scholar
  60. J.-E. Kang, M. M. Lim, R. J. Bateman et al., “Amyloid-β dynamics are regulated by orexin and the sleep-wake cycle,” Science, vol. 326, no. 5955, pp. 1005–1007, 2009. View at: Publisher Site | Google Scholar
  61. L. Chen, J. Huang, L. Yang et al., “Sleep deprivation accelerates the progression of Alzheimer’s disease by influencing Aβ-related metabolism,” Neuroscience Letters, vol. 650, pp. 146–152, 2017. View at: Publisher Site | Google Scholar

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