Neuroimaging findings in primary insomnia.

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PATBIO-3132; No. of Pages 8 Pathologie Biologie xxx (2014) xxx–xxx

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Neuroimaging findings in primary insomnia E´tude de l’insomnie primaire par imagerie ce´re´brale J.N. O’Byrne a,b, M. Berman Rosa c, J.-P. Gouin c, T.T. Dang-Vu a,*,b,d a

Department of Exercise Science, Concordia University, 7141 Sherbrooke St W, Montreal, Quebec, H4B 1R6 Canada Center for Studies in Behavioral Neurobiology, Concordia University, 7141 Sherbrooke St W, Montreal, Quebec, H4B 1R6 Canada c Department of Psychology, Concordia University, 7141 Sherbrooke St W, Montreal, Quebec, H4B 1R6 Canada d Institut Universitaire de Ge´riatrie de Montreál, Universite´ de Montreál, 4565, chemin Queen-Mary, Montreal, Quebec, H3W 1W5 Canada b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 30 October 2013 Accepted 13 May 2014 Available online xxx

State-of-the-art neuroimaging techniques have accelerated progress in the study and understanding of sleep in humans. Neuroimaging studies in primary insomnia remain relatively few, considering the important prevalence of this disorder in the general population. This review examines the contribution of functional and structural neuroimaging to our current understanding of primary insomnia. Functional studies during sleep provided support for the hyperarousal theory of insomnia. Functional neuroimaging also revealed abnormalities in cognitive and emotional processing in primary insomnia. Results from structural studies suggest neuroanatomical alterations in primary insomnia, mostly in the hippocampus, anterior cingulate cortex and orbitofrontal cortex. However, these results are not well replicated across studies. A few magnetic resonance spectroscopy studies revealed abnormalities in neurotransmitter concentrations and bioenergetics in primary insomnia. The inconsistencies among neuroimaging findings on insomnia are likely due to clinical heterogeneity, differences in imaging and overall diversity of techniques and designs employed. Larger samples, replication, as well as innovative methodologies are necessary for the progression of this perplexing, yet promising area of research. ß 2014 Elsevier Masson SAS. All rights reserved.

Keywords: Insomnia Neuroimaging Sleep Sleep disorder Hyperarousal Positron emission tomography Single-photon emission computed tomography Magnetic resonance imaging Magnetic resonance spectroscopy

R E´ S U M E´

Mots cle´s : Insomnie Neuroimagerie Sommeil Troubles du sommeil Hyperactivation Tomographie par e´mission de positrons Tomographie d’e´mission monophotonique Imagerie par re´sonance magne´tique Spectroscopie en re´sonance magne´tique nucleáire

Les techniques d’imagerie ce´re´brale ont permis des avanceés conside´rables dans l’e´tude du sommeil chez l’humain. Cependant, les e´tudes par imagerie ce´re´brale dans l’insomnie primaire demeurent peu nombreuses, particulie`rement en regard de la pre´valence importante de ce trouble du sommeil dans la population geńe´rale. Cette revue examine la contribution des e´tudes d’imagerie ce´re´brale fonctionnelle et structurelle a` la compre´hension de l’insomnie primaire. Les e´tudes d’imagerie fonctionnelle au cours du sommeil appuient la theórie de l’hyperactivation dans l’insomnie. D’autres e´tudes fonctionnelles ont re´ve´le´ des alte´rations dans le traitement ce´re´bral des processus cognitifs et e´motionnels dans l’insomnie primaire. Les re´sultats des e´tudes structurelles sugge`rent des modifications neuroanatomiques, particulie`rement dans l’hippocampe, le cortex cingulaire ante´rieur et le cortex orbitofrontal. Cependant, ces re´sultats ne sont pas concordants d’une e´tude a` l’autre. Quelques e´tudes spectroscopiques ont re´ve´le´ des alte´rations dans les niveaux de neurotransmetteurs, ainsi que des changements bioeńerge´tiques dans l’insomnie primaire. Le manque de concordance entre les re´sultats d’imagerie ce´re´brale en insomnie pourrait eˆtre lie´ a` l’he´te´rogeńeíte´ des diffe´rentes populations cliniques e´tudieés, ainsi qu’a` la diversite´ des techniques d’imagerie et d’analyse employeés. La neuroimagerie constitue une voie d’exploration prometteuse de l’insomnie, mais la poursuite des avanceés dans ce domaine nećessite de reúnir de plus grands ećhantillons, de reproduire et confirmer les re´sultats existants, tout en de´veloppant l’utilisation de nouvelles modalite´s. ß 2014 Elsevier Masson SAS. Tous droits re´serve´s.

* Corresponding author. E-mail address: [email protected] (T.T. Dang-Vu). http://dx.doi.org/10.1016/j.patbio.2014.05.013 0369-8114/ß 2014 Elsevier Masson SAS. All rights reserved.

Please cite this article in press as: O’Byrne JN, et al. Neuroimaging findings in primary insomnia. Pathol Biol (Paris) (2014), http:// dx.doi.org/10.1016/j.patbio.2014.05.013

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1. Introduction Insomnia is a remarkably prevalent disorder. Depending on the definition used, it affects 6–20% of the general population [1–5]. As a result, sleep dissatisfaction counts among the most common health complaints in primary care [6] and the associated healthcare expenditures, in addition to the costs of sleep aids and absenteeism at work, contribute to a considerable economic burden [7,8]. Symptoms of insomnia include difficulties falling asleep and staying asleep, and feelings of non-restorative sleep [4]. Daytime fatigue, mood disruption and cognitive impairments associated with insomnia negatively affect productivity and quality of life [9–11]. While insomnia symptoms can be a transient response to stress or changes in sleep-wake schedule, 70% of individuals with insomnia display persistent symptoms for more than three months (i.e., chronic insomnia) [12]. Relatively few neuroimaging studies have examined the physiology of this common sleep disorder [13]. Neuroimaging techniques can be useful in identifying the cerebral mechanisms of insomnia pathogenesis, and the neural correlates of insomnia symptoms. In this paper, we review the findings of these pioneering studies, which examined insomnia through the lenses of single-photon emission computed tomography (SPECT), positron emission tomography (PET), magnetic resonance imaging (MRI), functional MRI (fMRI) and magnetic resonance spectroscopy (MRS). PET, SPECT and fMRI, are functional modalities that examine changes in brain metabolism, blood flow or blood oxygenation. Structural modalities, such as MRI and MRS, map out subtle changes in brain anatomy and content. In synthesizing the strengths and limitations of these studies, we propose future directions in this expanding area of research. The scope of this review will be limited to primary insomnia (PI), which is defined by sleep disturbances occurring in the absence of comorbid medical or psychological conditions [14].

Fig. 1. Primary insomia: functional studies. Regional cerebral metabolism during wake and NREM sleep in PI. Smith et al. [15,16] found reduced regional cerebral blood flow (SPECT) in the basal ganglia in insomniacs. Nofzinger et al. [17] found smaller reductions in regional metabolism (18F-FDG PET) during the transition to from wake to NREM sleep in patients with PI. Nofzinger et al. [18] found a correlation between WASO and metabolism in thalamocortical pathways and the pontine tegmentum. Altena et al. [19] and Nofzinger et al. [17] found evidence for prefrontal deactivation during wake. Adapted from Desseilles et al. [13], and from illustrations by Patrick J. Lynch and C. Carl Jaffe. http://creativecommons.org/ licenses/by/2.5/.

2. Functional neuroimaging 2.1. PET and SPECT The first neuroimaging studies to examine PI used PET and SPECT functional imaging techniques. PET and SPECT both involve the injection of a radiolabeled isotope (the tracer) into the bloodstream. Depending on the tracer employed, the scans can offer indices of cerebral blood flow, cerebral metabolic rate of glucose (CMRglu) or neurotransmission. Smith et al. [15] employed SPECT with technetium-99m-hexamethylpropylenamine oxime (99mTc-HMPAO), a gamma-emitting radionuclide imaging agent, in order to observe regional cerebral blood flow during non-rapid-eye-movement (NREM) sleep in 5 PI patients and 4 good sleepers, all 9 of them female. Compared to controls, PI patients displayed cerebral hypoperfusion during NREM sleep in eight pre-selected regions of interest. The most pronounced hypoperfusions were observed in the basal ganglia (Fig. 1), and to a lesser extent in the frontal medial, occipital and parietal cortices. In a later study, the same group re-scanned 4 of the 5 PI patients after 8 weeks of behavioral therapy for insomnia. They found that a 43% reduction in sleep onset latency after treatment was accompanied by a 24% restoration of regional cerebral blood flow, especially in the basal ganglia [16]. These changes were thought to represent normalization of sleep processes. The authors further speculated that increased sleep debt from partial sleep deprivation in PI may accentuate the normal cerebral deactivation during sleep, as a homeostatic compensatory mechanism. In contrast, the next functional study by Nofzinger et al. provided support for the hyperarousal theory of insomnia [17]. The

hyperarousal theory explains PI as a fundamental imbalance in the sleep-promoting and arousal systems, resulting in a state of global cortical and physiological arousal across the sleep-wake cycle [20,21]. In the study by Nofzinger et al., 7 men and women with PI were compared to 20 age- and gender-matched healthy controls during wakefulness and NREM sleep, using 18F-fludeoxyglucose (18F-FDG) PET in order to measure regional cerebral metabolism, indexed by CMRglu. In line with hyperarousal theory, PI patients relative to controls were found to have a smaller reduction in relative metabolism from wakefulness to NREM sleep in the ascending reticular activating system, hypothalamus, thalamus, hippocampus, anterior cingulate cortex (ACC), medial prefrontal and insular cortices (Fig. 1). In addition, PI patients had lower waking metabolism than healthy controls in cortical (bilateral frontal, left superior temporal, parietal and occipital cortices) and subcortical regions (thalamus, hypothalamus and brainstem reticular formation). Nofzinger et al.’s results lend support to Espie’s integrated psychobiological inhibition model [22], according to which heightened arousal in PI is attributable to the inhibition of normal cortical deactivation during the transition from waking to NREM sleep. This model at once explains two major symptoms of insomnia: difficulty falling asleep because of restricted sleep onset-related cortical inhibition and; difficulty staying asleep because of the same disinhibition occurring following arousals over the course of the night. These arousals would otherwise go unnoticed because of rapid cortical deactivation in normal sleep [22].


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Nofzinger’s research group extended their findings in another PET study using 18F-FDG to examine CMRglu in relation to wake after sleep onset (WASO) in a sample of 15 patients with PI [18]. They found positive correlations between WASO, and increased glucose metabolism during NREM sleep in the thalamus, ACC, temporal and frontal cortices, as well as in the pontine tegmentum (Fig. 1). Conspicuously, these regions largely overlap with those regions from their previous study showing less reduction in the transition from waking to NREM sleep in PI. 2.2. Functional MRI studies Functional imaging can be obtained from blood-oxygen-level dependent (BOLD) contrast fMRI. The BOLD signal is sensitive to the relative decrease in deoxyhemoglobin concentration that follows the local increase in cerebral blood flow in an activated brain area. Functional MRI does not require the injection of a tracer, but requires that the subject lies in the scanner in real-time during the period of interest. A first fMRI study on insomnia by Altena et al. [19] examined BOLD response during the completion of a verbal fluency task. Previous research alludes to cognitive dysfunction in insomnia [23], but behavioral studies have yielded inconsistent results [24,25]. In order to detect cerebral alterations during cognitive performance, the researchers asked 21 older adults with PI (17 females, mean age s.d.: 61 6.2 years) and 12 age-, sex- and education-matched controls to complete letter and category fluency tasks, with a counting-backwards task provided as a baseline. During the cognitive tasks, PI patients showed hypoactivation in the left prefrontal cortex and left inferior frontal gyrus, in comparison to good sleepers (Fig. 1). As part of this same fMRI study, PI patients underwent 6 weeks of multimodal non-pharmacological therapy, including cognitive behavioral therapy (CBT) and light exposure therapy. Post-therapy, sleep efficacy and sleep onset latency improved significantly in PI patients. Activation in PI patients was partially restored in the medial prefrontal cortex during the category fluency task, and in the inferior frontal gyrus during the letter fluency task. The authors concluded that individuals with PI are cognitively compromised, as shown by altered brain responses during task performance [19]. Furthermore, these effects on brain responses appear reversible with treatment. It should be noted that the generalization of this study is limited to older populations, given the sexagenarian mean age of the participants. To further investigate cognitive impairments in PI, Drummond et al. scanned a sample of 25 young adults PI patients (12 females, mean age s.d.: 32.3 7.2 years) and 25 controls matched for sex, age and education, while they completed an N-back working memory task [26]. During the N-back task, individuals with PI showed less activation than good sleepers in the thalamus, fronto-parietal cortex and cerebellum, brain regions normally associated with working memory and motor and visual processing. No relationship was observed between task difficulty and brain activation for PI patients, whereas good sleepers showed increasing activation in these regions in direct proportion to task difficulty. This indicates that insomniacs failed to recruit brain areas typically engaged for performance of this task. Furthermore, good sleepers showed deactivation of the middle frontal gyrus, posterior cingulate and orbital frontal gyrus, brain regions involved in the ‘default mode’ network, with increasing task difficulty, whereas PI patients showed no change in these regions. The default mode network is composed of brain regions active when the brain is not otherwise engaged in goal-oriented behavior [27]. Lack of deactivation of the default mode network may indicate an inability to deactivate task-irrelevant brain regions during performance. The data support a PI cognitive task-performance profile characterized by failure to engage task-appropriate processes, while failing to disengage task-irrelevant processes. Actual performance in the task was however, unchanged. These data may explain the frequent

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subjective PI complaints of reduced cognitive performance, in the absence of actual deficits [26]. Insomnia is often comorbid with emotional disorders, and elevated emotional reactivity is thought to represent an important factor in the etiology of insomnia [28–30]. Accordingly, Huang et al. investigated abnormalities in PI emotional processing using resting-state fMRI connectivity analysis [31]. Resting-state fMRI has emerged as a useful tool for identifying broadly connected functional networks. The researchers compared 10 medicationnaı¨ve PI participants with 10 age- and sex-matched healthy controls during wakefulness. They found several abnormalities in PI emotional network connectivity compared to controls. Specifically, functional connectivity between the amygdala and broadly distributed cortical and subcortical areas was altered in PI compared to controls. Furthermore, an observed increase in amygdala to premotor cortex, sensorimotor cortex connectivity was correlated with total Pittsburgh Sleep Quality Index (PSQI) score, a subjective measure of sleep quality. Greater connectivity between the amygdala, a fear and threat processing centre, and the premotor cortex, which prepares motor action in response to threat perception, may reflect a hyper-reactivity to perceived threat in PI patients [32,33]. An elevated threat response is consistent with the hyperarousal model of PI [31].

3. Structural neuroimaging The present section examines the findings that structural neuroimaging techniques (with MRI and MRS) have revealed concerning anatomical and molecular brain changes associated with PI. 3.1. Volumetric differences in the hippocampus Cognitive deficits have been observed in individuals with PI, including impairment in hippocampus-dependent memory consolidation [23,34]. Together with evidence of suppressed hippocampal neurogenesis in sleep-deprived rats [35,36], these findings imply possible structural changes in the brains of individuals with PI, particularly in the hippocampus. Riemann et al. [37] were among the first to employ structural neuroimaging to investigate neuroanatomical differences between good sleepers and individuals with PI. Using MRI (1.5 Tesla), they measured dorsolateral prefrontal cortex (dlPFC), hippocampus, orbitofrontal cortex (OFC), ACC and amygdala volumes in individuals with PI (n = 8, mean age s.d.: 48.4 16.3 years) compared to controls. Of these regions, the hippocampus was found to be significantly reduced in the PI group (Fig. 2). This finding, however, was rendered non-significant upon familywise correction for multiple comparisons. Winkelman et al. [38] also scanned for differences in hippocampal volume between normal sleepers and individuals suffering from PI. Their study included 20 PI (mean age s.d.: 39.3 8.7 years) and 15 controls and employed a 3.0 Tesla MRI scanner. Contrary to Riemann et al. [37], no differences in hippocampal volume between the PI group and healthy controls were observed. However, in the PI group, there was a correlation between reduced volumes in the bilateral hippocampus and actigraphy-derived poor sleep efficiency and increased WASO. These three findings were later corroborated in a retrospective study by the same group [39], examining two independent samples from previous studies [38,44] totalling 41 PI patients and 35 controls. A recent study by Noh et al., [40] obtained MRI (1.5 Tesla) data from 20 physician-referred PI subjects (18 females; mean age s.d.: 50.8 10.8 years) and 20 healthy sleepers. Similar to the results of Winkelman et al. [38], the volumes of the left and right hippocampi in individuals with PI did not significantly differ from


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analysis revealed that, compared to healthy controls, both independent PI samples presented with significantly larger rACC volumes (Fig. 2). Furthermore, this increase in volume in the rACC was positively correlated with sleep onset latency and WASO. That is, a larger volume in this region was associated with worse sleep [39]. In addition, in the course of the original MRS study [44] (described below), the PI patients in the second sample were found to have significantly lower levels of inhibitory neurotransmitters in the ACC than controls. The ACC is believed to be involved in cognitive and emotional processing [47], and alterations in rACC volume have been associated with major depressive disorder [48]. The increases in rACC volume and altered neurotransmission observed may then relate to emotional dysregulation in PI [28], as well as to high comorbidity of PI with depression [30]. 3.3. Fronto-parietal volumetric differences

Fig. 2. Primary insomnia: structural studies. Volumetric differences and differences in gray matter volume in PI compared to good sleeper controls. Riemann et al. [37] found that the hippocampus was significantly smaller in PI patients, but this finding was not replicated [38–43]. Winkelman et al. [39] found the ACC to be enlarged in PI. Altena et al. [42] and Joo et al. [43] found reductions in gray matter densities in different neocortical areas. Adapted from Desseilles et al. [13], and from illustrations by Patrick J. Lynch and C. Carl Jaffe. http://creativecommons.org/ licenses/by/2.5/.

that of healthy controls. However, it was found that smaller volumes of either the left or right hippocampus inversely correlated with insomnia duration and polysomnography- (PSG-) defined arousal index. Participants also completed a battery of neuropsychological tests. In accordance with previous findings [45], individuals with PI had poorer performance in tests for attention, working memory, verbal and visual memory compared to controls. Lower hippocampal volumes were associated with decreased cognitive performances. Spiegelhalder et al. [41] investigated neuroanatomical changes in a clinically-referred sample of patients with PI using MRI. Their sample consisted of 28 patients (18 females, mean age s.d.: 43.7 14.2 years) diagnosed with PI and 38 good sleeper controls. Unlike Riemann et al. [37], their analyses revealed no statistically significant between-group differences in hippocampal volumes. Furthermore, no significant correlations were found between selfreported measure of insomnia severity or total sleep time and left or right hippocampal volumes. Some differences among studies regarding the anatomical delineation of the hippocampus should be noted. Whereas Riemann et al. [37] included the alveus, fimbria and hippocampal-amygdala transition area (HATA) in calculating hippocampal volume, Noh et al. [40] excluded the fimbria and HATA, and Winkelman et al. [38,39] excluded all three areas. Such methodological inconsistencies may in part be responsible for the discrepant findings about hippocampal volume in PI. 3.2. Volumetric differences in the ACC Nofzinger et al. [17,21] identified the ACC as an area of interest in the neurobiology of insomnia. In their PET study, the ACC was among the regions that showed smaller reductions in activation from wakefulness to sleep in PI patients than in controls. This finding was corroborated by an observed reduction in GABA in this region in PI subjects relative to controls [46]. Winkelman et al. [39] retrospectively analyzed MRI data collected from two independent studies (i.e., [38] and [46]) with comparable designs and sample characteristics. The primary area of interest investigated was the bilateral rostral ACC (rACC). Morphometric

With the use of voxel-based morphometry (VBM), Altena et al. [42] examined whether volumetric differences in white and gray matter concentrations existed between a PI group composed of 24 participants (17 females, mean age s.d.: 60.3 6.0 years) and a control group comprising 13 good sleepers. Individuals with insomnia had smaller volumes of gray matter in three areas: the left OFC, the bilateral anterior precuneus of the parietal cortex and the bilateral posterior precuneus in the occipitoparietal cortex, compared to controls (Fig. 2). Notably, the reduction in the OFC was still significant after familywise correction for multiple comparisons. A negative correlation between insomnia severity, as measured by the Sleep Disorder Questionnaire, and left OFC volume was observed. The reduction in hippocampal volume observed by Riemann et al. [37] was not replicated. Joo et al. [43] also used VBM to analyze differences in gray and white matter volumes between a PI cohort (n = 27, 25 females, mean age s.d.: 52.3 7.8 years) and a healthy control group. In contrast to earlier VBM studies, the researchers employed SPM8based VBM, which introduces a registration method termed Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra (DARTEL) to the morphometric analysis. DARTEL increases the sensitivity and accuracy of VBM in the detection of differences in gray and white matter volumetric composition. In order to assess the cognitive features associated with PI and their possible relationship with neuroanatomical changes, participants were asked to complete several neuropsychological tests assessing attention, working memory, executive function and verbal function. Imaging analysis revealed a significant reduction of gray matter concentration in the dlPFC of PI subjects compared to controls (Fig. 2). This included the bilateral superior, middle and inferior frontal gyri. The gray matter decrements also extended to the OFC, in line with the findings reported by Altena et al. [42]. In addition, individuals with PI scored significantly lower than controls on tests of attention, and nonverbal memory. Worse cognitive performance on nonverbal memory tasks was correlated with PSG-derived shorter total sleep time and poorer sleep efficiency, indicating a link between poor sleep and memory dysfunction. Furthermore, a negative correlation was found between gray matter concentrations in the left middle frontal gyrus and the Insomnia Severity Index. There was however no correlation between cognitive performances and grey matter concentrations. It should be noted that none of the group differences in gray matter concentrations remained significant after correction for multiple comparisons. Spiegelhalder et al. [41] investigated fronto-parietal volumetric differences in a large sample (PI: 28; controls: 38). Images were acquired using a 3.0-Tesla MRI scanner and were subsequently analyzed via VBM using DARTEL registration for differences in gray and white matter concentrations. In contrast with findings by Joo


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et al. [43] and Altena et al. [42], VBM analysis revealed no significant between-group differences in gray and white matter concentrations. There is little agreement in the white and gray matter concentration data, with the exception that both Altena et al. [42] and Joo et al. [43] found reduced gray matter concentrations in the OFC of individuals with PI, compared to controls. Spiegelhalder et al. [41] found no such difference in a larger sample, however. Still, the observed prefrontal neuroanatomical changes may hold significance with regard to PI cognition. This hypothesis is tempered by the lack of any correlation between gray matter deficits and neuropsychological scores [43], so it is equally likely that these deficits relate instead to other processes associated with PI, such as emotional dysregulation [28]. 3.4. MRS studies In vivo MRS is a non-invasive method allowing the estimation of relative concentrations of specific molecules in the brain. Building on recent neuroimaging advances, Winkelman et al. utilized single-voxel proton MRS (1H-MRS) to compare daytime in vivo levels of gamma-aminobutyric acid (GABA) neurotransmission in 16 PI patients (8 females, mean age s.d.: 37.3 8.1 years) and 16 healthy controls matched for age and sex [49]. Winkelman et al. determined that global GABA levels were 30% lower in PI patients than in healthy controls. Lower global GABA levels were also associated with higher PSGquantified WASO within the PI group. As GABA is the primary inhibitory neurotransmitter in the human brain, deficits in GABA are likely to result in difficulty regulating cortical arousal at night, in accord with hyperarousal theory. It is important to note that this first study averaged the entire brain GABA concentration into one global index lacking spatial resolution. A subsequent study from the same group utilized 1H-MRS to estimate differences in GABA levels among PI patients with greater spatial specificity [44,50]. In a new sample of 20 PI patients and 20 age- and sex-matched controls, Plante et al. obtained results consistent with their first study. Among PI patients, GABA levels were significantly lower in the occipital cortex by 33% and in the ACC by 21%, but were unchanged in the thalamus, compared to good sleepers [44]. In contrast, Morgan et al. [50] observed that GABA levels were 12% higher in the occipital cortex of PI patients compared to good sleepers. Morgan et al. [50] also detected a negative correlation between global GABA levels and PSGmeasured WASO across both groups. Plante et al. [46] noted about this apparent discrepancy that there was a limited overlap between the voxels each study used to measure GABA levels in the occipital cortex. One last functional neuroimaging study used MRS with phosphorous (31P-MRS) to investigate differences in brain energetic compounds such as phosphocreatine among PI patients and controls [51]. Harper et al. found that 16 PI patients showed reductions in gray matter phosphocreatine, compared to 16 good sleepers. This might indicate that insomniacs experience a greater cortical energy demand than normal sleepers, which is consistent with a continual state of hyperarousal in insomnia. 4. Conclusions In spite of a recent increase in neuroimaging research into PI, we have yet to glean a consistent story about its neuropathology, especially with regard to structural studies of brain alterations. Functional studies are too few and diverse in methodology to yield any general conclusions, whereas results of structural studies are

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either contradictory or require replication. Tables 1 (functional) and 2 (structural) provide summaries of the main results of each study reviewed. The data tend toward an agreement with the hyperarousal concept of insomnia, whereby PI is the result of a chronic state of central nervous system (CNS) arousal that prevents and disturbs sleep. Reduced deactivation in the transition from wakefulness to sleep [17], heightened connectivity of the emotional and threat response systems [31], diminished modulation of irrelevant cognitive processes [26], depletion of inhibitory neurotransmission [44,49,50] and increased brain energetic demands [51] all are consistent with an inability to appropriately decrease CNS arousal during sleep onset and maintenance. Structurally, PI reduction in hippocampal volume was found in one study [37] and was not successfully replicated [38,40–42]. Lower gray matter concentrations were observed in the prefrontal cortex, specifically in the OFC [42,43] and in the dlPFC [43], but again, they were not replicated by a later study [41]. Rostral ACC volumes were increased in two independent PI samples [39], and interestingly, GABA levels in the ACC were significantly decreased in one of these samples [44]. Globally, GABA levels in the PI brain seem significantly diminished [49]; however, findings concerning GABA levels in the occipital cortex conflict [44,50]. Lastly, lower phosphocreatine concentrations in the gray matter of individuals with PI seem to indicate altered cortical energetic demands [51]. Overall, these promising structural findings are in need of replication. Variability in sampling and methodology across studies may explain the variability in findings (Tables 1 and 2). Sample characteristics such as mean age and sex-ratio varied extensively across studies. Differences in diagnostic assessment of PI and patient history of pharmacological treatment may also have contributed to cross-study variability. Furthermore, differences in PI severity and duration may also account for the discrepancies observed in the literature. Individuals who experienced insomniarelated sleep deprivation over many years may differ from those with more recent insomnia. The diagnostic criteria for PI encompass a heterogeneous group of individuals. The DSM-IV insomnia subtypes have not received strong empirical support [52] and have been removed from the DSM-V. Nonetheless, there is considerable variability in the extent to which the subjective sleep complaints are associated with PSG-derived objective sleep disturbances. Indeed, some studies found correlations between brain alterations and PSG-defined sleep disturbances [38,39,43,50], while others found correlations with subjective parameters of severity [39,43]. The difficulty in identifying empirically-supported subtypes of insomnia may also explain the variable results obtained with neuroimaging. In structural studies, the norms for anatomically delineating brain regions of interest and the subsequent methods of morphometric analysis also varied [37,38,40]. Future studies should pay careful attention to previous methodologies when attempting to replicate findings and ensure comparable sample characteristics and methods of analysis. Greater sample sizes are also needed in order to improve statistical power. Inconsistencies among the neuroimaging findings to date may seem daunting to further research. To the contrary, such disagreement in the data should spur a still more structured and systematic program of neuroimaging research on insomnia, using balanced samples, consistent diagnostic criteria and methodologies, adequate subjective and objective sleep measures, larger sample sizes and importantly, replication of existing findings. In addition, new imaging modalities, such as connectivity analyses and cortical thickness measurements, may shed light on the question of brain modifications in primary insomnia.


Neuroimaging technique

Sample size (number females) PI

of

Mean age in years (s.d.)

PI duration

History of pharmacological treatment

Main findings in PI compared to GS (significance level)

Hypoperfusion of basal ganglia and other regions during NREM sleep (P 0.05, uncorr.) Partial reestablishment of activation in basal ganglia after BT (P 0.05, uncorr.) Smaller reduction in glucose metabolism during transition to sleep; prefrontal hypoactivation during wake (P 0.05, corr.) Correlation between WASO and thalamocortical activation, including pontine tegmentum (P < 0.05, corr.) Prefrontal hypoactivation during verbal fluency task, partially restored after CBT (P < 0.05, uncorr.) Altered connectivity in amygdalar pathways, particularly to the premotor cortex; amygdala-premotor connectivity was correlated to PSQI (P < 0.05, corr.) During cognitive task, reduced activation in task-relevant areas and reduced deactivation of default mode regions (P < 0.05, uncorr.)

GS

PI

GS

Smith et al., 2002 [15]

99m

TC-HMPAO SPECT

5 (5)

4 (4)

37.8 (12.1)

34.5 (11.9)

ICSD-2, PSG

> 6 mo

Off sleep aids for > 4 weeks, off SSRIs for > 1 yr

Smith et al., 2005 [16]

99m TC-HMPAO SPECT 18 F-FDG PET

4 (4)

None

34.5 (12)

None

ICSD-2, PSG

> 6 mo

7 (4)

20 (13)

34.2 (8.9)

32.6 (8.4)

DSM-IV, PSG

1 mo

Off sleep aids for > 4 weeks, off SSRIs for > 1 yr PI using med. were excluded

15 (7)

None

36.9 (10.5)

None

DSM-IV, PSG

1 mo

PI using med. were excluded

60 (8.2)

RDC, PSG

2.5 yrs

Off med. for 2 mo

Nofzinger et al., 2004 [17]

Nofzinger et al., 2006 [21]

18

Altena et al., 2008 [22]

fMRI (1.5 T)

21 (17)

12 (9)

61 (6.2)

Huang et al., 2012 [31]

fMRI (3.0 T)

10 (5)

10 (5)

37.5 (12.4)

35.5 (8.7)

DSM-IV, PSG

n.r.

Medication-naive

Drummond et al., 2013 [26]

fMRI (3.0 T)

25 (12)

25 (12)

32.3 (7.2)

32.4 (7.1)

DSISD, actigraphy, PSG

3 mo

PI using med. were excluded

F-FDG PET

corr: corrected for multiple comparisons; uncorr.: uncorrected for multiple comparisons; n.r.: not reported; s.d.: standard deviation; PI: primary insomnia; GS: good sleeper controls; NREM: non-rapid-eye-movement; BT: behavior therapy; CBT: cognitive behavioral therapy; PSQI: Pittsburgh Sleep Quality Index; PSG: polysomnography; WASO: wake after sleep onset; ACC: anterior cingulate cortex; med.: medication; SSRI: selective serotonin reuptake inhibitor; CNS: central nervous system; DSM-IV: Diagnostic and Statistical Manual of Mental Disorders, edition IV; ICSD-2: International Classification of Sleep Disorders-2; RDC: Research Diagnostic Criteria for insomnia; DSISD: Duke Structured Interview for Sleep Disorders; mo.: months; yrs: years.

J.N. O’Byrne et al. / Pathologie Biologie xxx (2014) xxx–xxx

PI diagnosis and assessment

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Table 1 Functional neuroimaging studies of insomnia.

Mean PI duration in yrs (s.d.)

History of pharmacological treatment

Main findings in PI compared to GS (significance level)

46.3 (14.3) 60.2 (8.4)

DSM-IV, PSG DSM-IV

11.6 (8.9) 17.7 (15.8)

Reduction in HCV (P 0.05, uncorr.) Reduction in GMCs in OFC, (P < 0.05 corr.)

39.3 (8.7)

38.8 (5.3)

DSM-IV, actigraphy, PSG

> 6 mo

20 (18)

50.8 (10.8)

50.4 (11.7)

ICSD, PSG

7.6 (6.1)

Off med. for 2 weeks Off hypnotic med. for 2 mo No regular (> 1/week) treatment with CNSactive med. for 3 mo No hypnotic med. for 1 mo

27 (25)

27 (23)

52.3 (7.8)

51.7 (5.4)

ICSD-2, PSG

7.6 (6.1)

Medication-naive

VBM-MRI (3.0 T) with DARTEL

28 (18)

38 (21)

43.7 (14.2)

39.6 (8.9)

DSM-IV, PSG

12.1 (11.0)

Off psychoactive med. for 2 weeks

Winkelman et al., 2013 (Study 1) [39]

MRI (3.0 T)

20 (10)

15 (6)

39.3 (8.7)

38.8 (5.3)

DSM-IV, PSG

6 mo

Off CNS-active med. for 2 weeks

Winkelman et al., 2013 (Study 2) [39] Winkelman et al., 2008 [49]

MRI (3.0 T)

21 (14)

20 (12)

35.8 (9.5)

34.1 (9.9)

DSM-IV, PSG

6 mo

1

16 (8)

16 (7)

37.3 (8.1)

37.6 (4.5)

DSM-IV, actigraphy, PSG

6 mo

Morgan et al., 2012 [50]

1

16 (10)

17 (9)

39 (9)

36 (9)

DSM-IV, PSG

1 yr

Plante et al., 2012 [44]

1

20 (12)

20 (12)

34.3 (8.3)

34.1 (9.9)

6 mo

Harper et al., 2013 [51]

31

16 (8)

16 (7)

37.2 (8.4)

37.6 (4.7)

DSM-IV, actigraphy, PSG DSM-IV, actigraphy, PSG

Off CNS-active med. for 2 weeks No regular (> 1/week) treatment with CNSactive med. for 3 mo Off CNS-active med. for > 3 mo Off CNS-active med. for > 2 weeks Off CNS-active med. for > 3 mo

Neuroimaging technique

PI

GS

PI

GS

Riemann et al., 2007 [37] Altena et al., 2010 [47]

MRI (1.5 T) VBM-MRI (1.5 T)

8 (5) 24 (17)

8 (5) 13 (9)

48.4 (16.3) 60.3 (6.0)

Winkelman et al., 2010 [38]

MRI (3.0 T)

20 (10)

15 (6)

Noh et al., 2012 [41]

MRI (1.5 T)

20 (18)

Joo et al., 2013 [48]

VBM-MRI (1.5 T) with DARTEL

Spiegelhalder et al., 2013 [43]

H-MRS (4.0 T)

H-MRS (4.0 T) H-MRS (4.0 T) P-MRS (4.0 T)

Sample size (number of females)

> 6 mo

No diff. in HCV. SE and WASO negatively correlated with HCV (P 0.05) No diff. in HCV. HCV negatively correlated with higher arousal index (P < 0.05) and longer insomnia duration (P < 0.001) Reduction in GMCs in dlPFC and OFC. Negative correlations between: left middle frontal gyrus GMCs and ISI; right postcentral gyrus GMCs and SOL; right precentral gyrus GMCs and WASO (P 0.001 uncorr.) No diff. in HCV, no diff. in GMCs and WMCs. No correlation between ISI and HCV, nor between HCV and total sleep time (P < 0.05, corr. and P < 0.001, uncorr.) Increased rACC volume. rACC volume correlated positively with SOL and WASO, negatively with SE (P 0.05, uncorr.) Increased rACC volume. Right ACC volume correlated with SOL (p 0.05, uncorr.) 30% lower global GABA levels (P = 0.039)

12% higher levels of GABA in the occipital cortex (P < 0.05) 33% lower global GABA levels in occipital cortex, 21% lower in ACC (P < 0.05) Lower phosphocreatine in gray matter, (P < 0.05, corr.)

corr.: corrected for multiple comparisons; uncorr.: uncorrected for multiple comparisons; s.d.: standard deviation; diff.: difference; NA: not applicable; PI: primary insomnia; GS: good sleeper controls; HCV: hippocampal volume; GMC: gray matter concentrations; WMC: white matter concentration; rACC: rostral anterior cingulate cortex; PC: parietal cortex; dlPFC: dorsolateral prefrontal cortex; ISI: Insomnia Severity Scale; PSG: polysomnography; WASO: wake after sleep onset; SOL: sleep onset latency; SE: sleep efficiency; med.: medication; CNS: central nervous system; GABA: gamma-aminobutyric acid; DSM-IV: Diagnostic and Statistical Manual of Mental Disorders, edition IV; ICSD-2: International Classification of Sleep Disorders-2; mo.: months; yrs: years.

G Model

PATBIO-3132; No. of Pages 8

PI diagnosis and assessment

Mean age in years (s.d.)

Study

J.N. O’Byrne et al. / Pathologie Biologie xxx (2014) xxx–xxx


Table 2 Structural neuroimaging studies of insomnia.

7

G Model


8

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