Cortical and Subcortical Brain Changes in Children and Adolescents With Narcolepsy Type 1

Cortical and Subcortical Brain Changes in Children and Adolescents With Narcolepsy Type 1 Abstract Study Objectives Neuroimaging studies on structural alterations in patients with type 1 narcolepsy (NT1) have shown controversial and heterogeneous results. The purpose of this study was to investigate microstructural brain changes in patients with NT1 close to disease onset. Methods We examined cortical and subcortical grey matter volumes in 20 drug-naïve children and adolescents with NT1 compared with 19 healthy controls; whole-brain voxel-based morphometry, shape and volumetric analyses, and cortical thickness analysis were used. Results When compared with controls, NT1 patients revealed reduced grey matter volume in cerebellum and medial prefrontal cortex and increased volume in right hippocampus. Cortical thickness in frontal lobe was also reduced in patients compared with controls. Increased volume and shape expansion in right hippocampus in patients compared with controls were also confirmed by both vertex and volumetric analyses. Conclusions Our results indicate that subtle structural brain changes involving attentional and limbic circuits are detectable in children and adolescents with NT1. Cerebellum involvement might be related to the childhood NT1 clinical phenotype. narcolepsy, structural neuroimaging, voxel-based morphometry, shape analysis, medial prefrontal cortex, hippocampus Statement of Significance Although localized brain alterations in type 1 narcolepsy (NT1) patients have been postulated by systematic structural neuroimaging techniques, results remain controversial and potentially affected by long period of brain adaptation to the disease. The present study reveals for the first time that subtle brain changes occur early in the disease and independently of clinical course or duration; these alterations are located in key structures for attentional and limbic functions and also involve cerebellar circuits suggesting the presence of a specific clinical phenotype of NT1 in childhood. Additional studies are necessary to investigate the correlation between brain alterations and behavioural, cognitive, and motor abilities in young patients. Introduction Type 1 narcolepsy (NT1) is a lifelong disorder characterized by excessive daytime sleepiness (EDS), cataplexy, sleep paralysis, hypnagogic hallucinations, and nocturnal sleep fragmentation [1,2].It occurs in 0.025%–0.05% of the population, with peak incidence between the age of 10 and 19 years [3],although recent reports seem to indicate an earlier onset [4]. NT1 is caused by the loss of neurons that produce hypocretin in the posterolateral hypothalamus leading to low or absent hypocretin in cerebrospinal fluid and to a dysregulation of sleep and wakefulness systems [5,6]. Although the use of conventional magnetic resonance imaging (MRI) did not show consistent macroscopic lesion in NT1, systematic structural neuroimaging techniques such as voxel-based morphometry (VBM) have evidenced localized brain alterations in NT1 patients, even if with controversial results [7,8]. Several studies have found decrease of grey matter (GM) in hypothalamic structures in NT1 patients compared with healthy subjects [9–13], which is in agreement with a hypocretinergic neurotransmission dysfunction. On the contrary, other studies failed to detect hypothalamic changes but reported cortical GM decreases in frontotemporal areas [14,15], possibly related to cognitive and affective disorders typical of narcoleptic patients. Additionally, alterations were also reported in nucleus accumbens [10,11], thalamus [11,12], and mesial temporal lobe structures [16,17]. Furthermore, increases of cortical volume and thickness were demonstrated in areas of dorsolateral prefrontal cortex, suggesting compensatory processes to sustain cognitive performances in patients [18]. In conclusion, several discrepancies are evident in previous studies, both in terms of brain regions involved and direction (increases vs decreases) of volumetric changes; this heterogeneity could potentially be due to large variability in patients population, analysis methods, different disease duration and age of disease onset, and pharmacological treatment. A recent meta-analysis of VBM studies in NT1 patients failed to detect consistent results reporting no evidence of spatially consistent localized loss of GM in narcolepsy [19]. If we consider the mean age of subjects included in MRI investigations, all the above studies were performed in adult patients, therefore several years or even decades after the disease onset [4]. Indeed, because of general unawareness of this disorder and to the difficulty in diagnosis, the median time to NT1 diagnosis has been reported to be as high as 10.5 years [20] leading to a characterization of the disease after a long period of adaptation since onset. To the best of our knowledge, no neuroimaging studies have explored structural brain changes in children and adolescents with a diagnosis of NT1. It is well known that NTI in childhood and adolescence has specific features [21]. Plazzi and colleagues demonstrated the presence of complex movement disorders at disease onset that seem transient and vanish later in the course of the disease [22]. In addition, personality and behavioural changes occur early in the disease and children with NT1 have often features of depression and attention’s deficits suggesting the involvement of limbic structures. These behavioural disturbances may change during the course of the disease and its pharmacological treatment [23]. The purpose of our study was to investigate structural cortical and subcortical brain alterations in drug-naïve children and adolescents with NT1, in order to detect potential early structural brain changes at disease onset. Methods Subjects Twenty drug-naïve patients with a diagnosis of NT1 were recruited (13 males, mean age 12.15 ± 3.06 years). Patients were proposed to participate in the study if the following inclusion criteria were satisfied: (1) drug-naïve children with NT1; (2) diagnosis of NT1 according to the current criteria 2; (3) proven CSF hypocretin-1 deficiency; and (4) video documentation of cataplectic attacks during a standardized video-session [24,25] prior to the neuroimaging experimental sessions. All subjects were right-handed [26]. Table 1 reports patients’ clinical data. All patients had a complete clinical and neurological examination, conventional brain MRI, and underwent a 48-hour continuous polysomnographic recording followed by a standard five-nap Multiple Sleep Latency Test (MSLT) [27] and video recordings of cataplexy as described previously[24,25]. HLA-DQB1*0602 typing was conducted in all patients. Cataplexy frequency was assessed and subjective sleepiness was quantified by means of a modified version of the Epworth sleepiness scale (mESS) [28]. Table 1. Clinical and Laboratory Data of NT1 Patients Pt ID  Age (yr)  Gender  Disease duration (yr)  CSF hcrt  Cataplexy frequency  Hypotonia  Cataplectic facies  mESS score  MSLT-SL  MSLT-SOREMPs  1  17  F  1.6  0.0  1w–1d  no  no  15  2.6  5  2  13  F  2.0  10.7  1w–1d  no  no  13  3.30  5  3  14  M  2.8  39.8  1d  no  yes  18  5.50  5  4  11  M  3.7  43.1  1y–1m  yes  yes  13  9.00  5  5  14  F  3.2  0.0  1w–1d  no  yes  12  2.40  5  6  10  M  1.7  0.0  >1d  yes  yes  21  1.90  4  7  10  F  3.7  15.5  >1d  no  no  14  1.20  1  8  10  M  0.8  15.7  >1d  yes  yes  13  11.6  3  9  15  M  1.0  0.0  1w–1d  no  no  17  3.30  4  10  15  M  0.3  10.9  1w–1d  yes  no  16  6.40  4  11  16  M  4.5  15.5  1m–1w  yes  yes  8  5.50  3  12  11  F  1.3  0.0  1w–1d  yes  yes  14  1.00  5  13  11  M  3.3  25.0  >1d  no  no  14  9.80  4  14  9  M  1.0  35.4  1w–1d  yes  yes  9  3.40  5  15  13  M  2.7  55.0  n.r.  no  no  n.r.  13.00  4  16  8  M  0.3  0.0  1w–1d  yes  yes  10  7.50  3  17  11  F  1.2  84.2  >1d  no  no  12  8.90  3  18  8  M  1.0  0.0  >1d  yes  yes  17  4.8  2  19  7  F  0.9  0.0  >1d  yes  yes  20  9.60  1  20  16  M  6.1  0.0  1w–1d  no  no  14  2.40  5  Pt ID  Age (yr)  Gender  Disease duration (yr)  CSF hcrt  Cataplexy frequency  Hypotonia  Cataplectic facies  mESS score  MSLT-SL  MSLT-SOREMPs  1  17  F  1.6  0.0  1w–1d  no  no  15  2.6  5  2  13  F  2.0  10.7  1w–1d  no  no  13  3.30  5  3  14  M  2.8  39.8  1d  no  yes  18  5.50  5  4  11  M  3.7  43.1  1y–1m  yes  yes  13  9.00  5  5  14  F  3.2  0.0  1w–1d  no  yes  12  2.40  5  6  10  M  1.7  0.0  >1d  yes  yes  21  1.90  4  7  10  F  3.7  15.5  >1d  no  no  14  1.20  1  8  10  M  0.8  15.7  >1d  yes  yes  13  11.6  3  9  15  M  1.0  0.0  1w–1d  no  no  17  3.30  4  10  15  M  0.3  10.9  1w–1d  yes  no  16  6.40  4  11  16  M  4.5  15.5  1m–1w  yes  yes  8  5.50  3  12  11  F  1.3  0.0  1w–1d  yes  yes  14  1.00  5  13  11  M  3.3  25.0  >1d  no  no  14  9.80  4  14  9  M  1.0  35.4  1w–1d  yes  yes  9  3.40  5  15  13  M  2.7  55.0  n.r.  no  no  n.r.  13.00  4  16  8  M  0.3  0.0  1w–1d  yes  yes  10  7.50  3  17  11  F  1.2  84.2  >1d  no  no  12  8.90  3  18  8  M  1.0  0.0  >1d  yes  yes  17  4.8  2  19  7  F  0.9  0.0  >1d  yes  yes  20  9.60  1  20  16  M  6.1  0.0  1w–1d  no  no  14  2.40  5  F = female; M = male; yr = years; min = minutes; MSLT = multiple sleep latency test; SOREMPs = sleep-onset rapid eye movement periods; hcrt = hypocretin-1 level in CSF (pathological levels < 110 pg/mL); ESS = Epworth Sleepiness Scale score (range 0–24, pathological score > 10). View Large For groups’ comparisons, 19 volunteers were recruited to serve as healthy controls (HCs). Healthy subjects had no history of neurological diseases. All subjects, both patients and controls, had normal MRI at visual inspection. The human ethic committee of the University of Modena and Reggio Emilia approved this study, and written informed consent was obtained from all the patients recruited and their parents. MRI Acquisition Three-dimensional (3D) T1-weighted MRI images were acquired using a 3-T Philips Intera MRI scanner (Best, The Netherlands). A SPGR pulse sequence (echo time [TE] = 4.6 milliseconds, repetition time [TR] = 9.9 milliseconds) was used. One hundred seventy contiguous sagittal slices were acquired (voxel size = 1 × 1 × 1 mm) and field of view was 240 mm with a matrix size of 256 × 256 × 170. A T2-weighted axial scan was also acquired to allow visual determination of vascular burden or tissue abnormalities. Statistical Analysis of Demographical and Clinical Variables Demographical and clinical characteristics of the subjects were analyzed using SPSS software, and parametric and nonparametric statistics were used as appropriate. Independent samples t tests were used to compare age between patients and controls; chi-square was used to compare the two groups according to sex. VBM Analyses Structural data were analyzed with FSL-VBM (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLVBM) [29], an optimized VBM protocol [30] carried out with FSL tools (FMRIB’s Software Library, FSL: http://www.fmrib.ox.ac.uk/fsl) [31]. First, structural images were brain-extracted and GM-segmented before being registered to the MNI 152 standard space using nonlinear registration (www.fmrib.ox.ac.uk/analysis/techrep). The resulting images were averaged and flipped along the x-axis to create a left–right symmetric, study-specific GM template. Second, all native GM images were nonlinearly registered to this study-specific template and “modulated” using the Jacobian of the warp field to correct for local expansion (or contraction) due to the nonlinear component of the spatial transformation. The modulated GM images were then smoothed with an isotropic Gaussian kernel with a σ of 3 mm. Finally, voxelwise general linear modeling (GLM) was applied using permutation-based nonparametric testing (5,000 permutations); threshold-free cluster enhancement (TFCE) correction for multiple comparisons [32] was used and the statistical threshold was set at p < .05. Age, sex, and intracranial volume (calculated with SIENAX tool; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/SIENA) were entered as confounding variables in the GLM so as to control for the potential effect of these variables. In addition, separated correlation analyses with GM volume and disease duration, CSF hypocretin level, MSLT, and mESS score were performed in the patients’ group; in all analyses, age, sex, and intracranial volume were entered as regressors and a statistical threshold of p < .05 was applied. Anatomical locations of significant volume loss were determined by reference to the Harvard-Oxford cortical and subcortical structural atlas integrated into FSLview (part of FSL) in addition to careful manual inspection and identification. Shape and Volumetric Analyses Complementary to the VBM analysis, we used a different methodology to investigate neuroanatomical alterations of subcortical structures specifically. For this purpose, we used FSL/FIRST software (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIRST), a model-based segmentation/registration tool proposed by Patenaude et al 33. It uses manually labeled brain image data as a training set that was provided by the Center for Morphometric Analysis (CMA), MGH, Boston. These training data comprise of 15 subcortical structures (bilateral thalamus, caudate, putamen, pallidum, hippocampus, amygdale, accumbens, and brainstem) that were outlined by trained operators on T1-weighted MR images from 336 subjects. It contains a wide age range (4.2–72 years) and both normal and pathologic brains. The FIRST algorithm automatically segments subcortical structures based on the shape and intensity variations of the respective structure, as learnt from a training set. It uses default setting for each structure, which has been optimized empirically. Once the brain images were processed through the FSL/FIRST pipeline, each segmentation was inspected and found satisfactory. Vertex analysis (v5.0.0) was performed using first_utils in a mode of operation that aims to assess group differences on a per-vertex basis (the meshes were reconstructed in native space) [33–35]. To assess group differences, voxelwise GLM was applied using permutation-based nonparametric testing; the results adjusted for age, sex, and intracranial volume were considered significant at p < .05 cluster-based family wise error (FWE) corrected. In addition, volumetrics for the subcortical structures were derived using the scripts included in the FIRST module. To compare subcortical measures between patients and controls, we conducted a univariate ANCOVA with subcortical structure volume value as the dependent variable, group diagnosis as fixed factor, and age, gender, and intracranial volume as covariates. SPSS software (IBM, Chicago, IL) was used for statistical analyses. All neuroanatomical measures were examined for normality using Shapiro–Wilk test and transformed appropriately if they violated assumptions of normality. False discovery rate (FDR) was used to correct for multiple comparisons, and a threshold of p < .05 estimated using SPSS command, according to Bejamini and Hochberg methods [36], was considered statistically significant. Cortical Thickness Analyses Scans were analyzed using standardized image toolbox (Freesurfer, version 5.0) [37], quality assurance (outlier detection based on interquartile of 1.5 standard deviations along with visual inspection of segmentations), and statistical methods. Briefly, the pipeline involves removal of nonbrain tissue, automated Talaraich transformation, segmentation of white matter and GM, tessellation of grey or white matter boundary, automated correction of topology defects, surface deformation to form the grey or white matter boundary and grey or cerebrospinal fluid boundary, and parcellation of cerebral cortex. Cortical thickness estimates were calculated as the distance between the grey or white matter border and the pial surface at each vertex. Cortical thickness values were averaged over each major brain lobe (frontal, temporal parietal, and occipital). In addition, single value labels extracted based on automatic algorithm [38,39] were calculated for more precise analyses. Statistical analyses were performed using SPSS software (IBM, Chicago, IL). All neuroanatomical measures were examined for normality using Shapiro–Wilk test and transformed appropriately if they violated assumptions of normality. To compare cortical measures between patients and controls, we conducted a univariate ANCOVA with each neuroanatomical value as the dependent variable, group diagnosis as fixed factor, and age, gender, education, and intracranial volume as covariates. FDR was used to correct for multiple comparisons, and a threshold of p < .05 estimated using SPSS command, according to Bejamini and Hochberg methods [36], was considered statistically significant. Results Demographical and Clinical Characteristics NT1 patients (n = 20) had a mean age of 11.9 years (±2.91; range 7–17); 13 were male (Table 1). Control subjects (n = 19) had a mean age of 13.05 years (±2.17; range 6–16); 11 were male. No statistical significant difference was found in age (p = .29) and gender (p = .74) between the two groups. Mean disease duration was 2.2 years. Imaging Results Voxel-Based Morphometry Group comparison analysis between patients and controls showed that patients had reduced GM volume in right anterior cingulate or paracingulate (BA9) and cerebellum (vermis and right cerebellum lobe) and greater GM volume in right hippocampus compared with controls (Figure 1, Table 2). Table 2. Regions of GM Differences Showed by VBM Group Comparison Analyses (p < .05 TFCE Corrected) Region  t-statistic  MNI coordinates      x  y  z  Healthy controls > patients   Cerebellum, vermis  4.23  0  −62  −30   R, cerebellum, anterior lobe    22  −56  −28   R, cerebellum, posterior lobe    34  −60  −44   R, paracingulate/medial frontal gyrus (BA9)  4.40  8  56  14  Healthy controls < patients   R, hippocampus  4.91  30  −14  −20   R, hippocampal–amygdaloid transition area    24  −10  −20  Region  t-statistic  MNI coordinates      x  y  z  Healthy controls > patients   Cerebellum, vermis  4.23  0  −62  −30   R, cerebellum, anterior lobe    22  −56  −28   R, cerebellum, posterior lobe    34  −60  −44   R, paracingulate/medial frontal gyrus (BA9)  4.40  8  56  14  Healthy controls < patients   R, hippocampus  4.91  30  −14  −20   R, hippocampal–amygdaloid transition area    24  −10  −20  R = right; MNI = Montreal Neurological Institute. View Large Figure 1. View largeDownload slide VBM group comparison results. In red/yellow are depicted areas of increased volume in patients compared with controls; in blue are depicted areas of decreased volume in patients compared with controls (p < .05 TFCE corrected); R = right; coordinates in MNI = Montreal Neurological Institute. Figure 1. View largeDownload slide VBM group comparison results. In red/yellow are depicted areas of increased volume in patients compared with controls; in blue are depicted areas of decreased volume in patients compared with controls (p < .05 TFCE corrected); R = right; coordinates in MNI = Montreal Neurological Institute. Shape and Vertex Analysis Shape analysis revealed areas of significant shape expansion in the dorsolateral right hippocampus, from the head to the tail, in patients compared with the HCs (p < .05 FWE corrected) (Figure 2). No areas of hippocampal shape contraction were found in patients compared with controls. Other structures did not show any morphological change. Figure 2. View largeDownload slide Results of shape analysis for the right hippocampus. The regions in orange represent the part of the right hippocampus shown to be greater in patients compared with controls (p < .05 FWE corrected). Figure 2. View largeDownload slide Results of shape analysis for the right hippocampus. The regions in orange represent the part of the right hippocampus shown to be greater in patients compared with controls (p < .05 FWE corrected). Volumetry Volumetric analyses confirmed a significant statistically difference in right hippocampus with greater hippocampal volume in patients compared with controls (f = 5.3; p = .026). No differences were detected for other subcortical measures investigated. Correlation Analyses No correlation between GM volume and measures of disease duration, CSF hypocretin level, mESS score, and MSLT scores was detected. Cortical Thickness Analysis Considering global lobar cortical thickness analyses, we found that patients had significant reduced cortical thickness in right frontal lobe compared with controls (f = 6.8; p = .015); no differences emerged in left frontal lobe and in bilateral parietal, temporal, and occipital lobe. When considering each single label (34 for each hemisphere) and correction for multiple comparisons, no statistically significant difference remained. Discussion This is the first study investigating cortical and subcortical structural brain changes in drug-naïve children and adolescents with NT1. The possibility to measure volumetric neural changes close to disease onset gives us the opportunity to reveal early neurodevelopmental features of the disease before any anatomical and behavioural adaptation and possible drug-induced effects occur. Indeed, recent observations seem to uncover a specific clinical phenotype in children close to disease onset [22,24,25] and to suggest that functional and anatomical alterations showed in adults might be related to the severity and the duration of the disease [17,18]. In our study, we found that NT1 patients had lower GM volume in cerebellum and medial prefrontal cortex (mPFC), reduced cortical thickness in frontal lobe, and higher right hippocampus volume compared with HCs. Notably, no hypothalamic volume alteration was evident. Concerning the finding of cerebellum atrophy in NT1 patients, this is in line with the evidence that NT1 in childhood when close to disease onset frequently manifests with movement disorders [25], such as generalized hypotonia [40], possibly reflecting a modulation of orexinergic nervous system in motor control through the spinocerebellum, as demonstrated in animal experiments [41]. Only one other structural neuroimaging study reported cerebellum involvement in narcolepsy [10], but did not provide further speculation about a possible role of cerebellum in narcolepsy with cataplexy. As suggested by Pizza et al., the childhood phenotype with movement disorder decreases over time, gradually evolving into classical presentation [24]; therefore, it is quite plausible that the cerebellar structural alteration emerged in this study might be specific of this population (close to disease onset) and not detectable in older patients. Longitudinal studies with MRI comparison at different age from the disease onset should be performed to better explain this issue. The role of frontal lobe, in particular mPFC and anterior cingulate cortex (ACC) in narcolepsy, is well known and several experimental studies indicate that this region may play a critical role in narcolepsy [8,42,43], receiving excitatory projections from hypocretin neurons through the thalamic nucleus [44]. The mPFC is a critical site through which positive emotions trigger cataplexy and innervates downstream brainstem region involved in the regulation of muscle tone [45]. We recently studied the brain correlates of cataplexy in drug-naïve children or adolescent with recent onset NT1 with functional MRI (fMRI) while viewing funny videos disclosing a key activation of the ventral mPFC during cataplexy [46]. The mPFC is also involved in attentional and working memory functions and might account for attention and memory deficits emerged in neuropsychological studies in NT1 patients [47]. Moreover, anterior cingulate, as part of the limbic system, participates in awareness and emotional processing and might be related to depression and emotional instability frequently observed in NT1 patients [23]. In line with literature findings, we found a reduction of GM volume in a region including medial frontal gyrus and paracingulate cortex in NT1 patients compared with controls, suggesting that this alteration may occur early in the disease and might be related to behavioural and cognitive changes observed also in pediatric and adolescent patients. Unfortunately, in our study, we did not have any neurocognitive and personality characteristics available to provide a correlation with neuroanatomical measures. With regards to the alterations observed in right hippocampus, we found that NT1 patients had greater GM volume compared with controls and this result was confirmed with both VBM and shape analysis approach. This finding is compelling with regard to previously published morphometric studies in patients with narcolepsy showing a volume reduction in bilateral hippocampi [17], but we have to consider that our population is unique in terms of age and disease onset and a direct comparison with literature data may be difficult and misleading. The role of hippocampus in sleep–wake regulation is demonstrated by dense innervations provided by the hypocretin-containing neurons to the medial septum or diagonal band of Broca, which has been suggested to control the hippocampal theta rhythm and associated learning and memory functions through its cholinergic and GABAergic projections to the hippocampus [48,49]. Moreover, treatment with Modafinil, the most commonly prescribed central nervous system stimulant for narcolepsy, changes the cerebral metabolism and cerebral perfusion of hippocampus in narcoleptics [50], suggesting a central role of this structure in sleep–wake rhythm. Despite evidences of hippocampal involvement in narcolepsy pathophysiology, the volumetric direction of change remains challenging to explain and, without cognitive and memory measures available, we can only speculate that the greater hippocampus volume in our patients might reflect a compensatory mechanism to maintain cognitive performances, as suggested by other studies in narcolepsy [18] and other neuropsychiatric syndromes [51]. Finally, we observed a relatively asymmetric predominance of brain alteration involving mainly the right hemisphere, both in terms of lower (mPFC and cerebellum) and higher (hippocampus) GM volumetric changes. A right predominance was also detected in BOLD response during cataplexy in a recent fMRI study [46], but it was principally attributed to the type of material used to elicit laughs rather than to the neurobiology of NT1. Functional asymmetries between the two hemispheres in attentional and vigilance networks were already postulated by Posner in 1990, when he described an alerting network which focused on brain stem arousal systems along with right hemisphere related to sustained vigilance [52]. There is also evidence that the right hemisphere is selectively involved in processing negative emotions and depressive mood [53], and several studies showed that NT1 children become more introverted and manifest features of depression [47,54]. Also electrophysiological techniques provided evidence of a functional deterioration of the fronto–temporo–parietal network of the right-hemispheric vigilance system in narcolepsy and a therapeutic effect of modafinil on the left hemisphere, probably less affected by the disease [55]. The current study had limitations that deserve discussion. Firstly, the study population is numerically limited but it is selectively focused on drug-naïve and close to disease onset subjects, and this represents a unique point of view for speculations about disease pathophysiological mechanisms. Secondly, we did not collect measures of cognitive and behavioural functions in NT1 patients; therefore, we were not able to investigate correlations between personality or cognitive abilities and structural findings. Finally, as negative finding, we have not observed volumetric changes of the hypothalamus. This result can be a consequence of the inability of our morphometric techniques to detect hypothalamic changes; as a matter of fact, FIRST tool does not include hypothalamus segmentation; therefore, we were not able to perform automated shape and volumetric analysis of this structure specifically. Future studies including manual or automated specific segmentation of hypothalamus could probably better address this issue, especially if using ultra high-field MRI (7 T). In conclusion, our study demonstrated the presence of subtle anatomical brain changes in narcolepsy at disease onset involving attentional and limbic circuits, which may be specific of the disease and possibly free of any adaptation or modulation induced by disease duration and/or severity. In addition, our results support the hypothesis of a specific clinical phenotype in children characterized by neural alterations in motor systems in agreement with clinical evidence of movement disorders in NT1 children close to disease onset. Larger and possibly longitudinal studies including cognitive and behavioural measures and treatment response will be required to confirm these findings and to better specify the link between different clinical subtypes and structural abnormalities. Disclosure Statement None declared. 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Cortical and Subcortical Brain Changes in Children and Adolescents With Narcolepsy Type 1

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Oxford University Press
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© Sleep Research Society 2017. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.
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0161-8105
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1550-9109
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10.1093/sleep/zsx192
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Abstract

Abstract Study Objectives Neuroimaging studies on structural alterations in patients with type 1 narcolepsy (NT1) have shown controversial and heterogeneous results. The purpose of this study was to investigate microstructural brain changes in patients with NT1 close to disease onset. Methods We examined cortical and subcortical grey matter volumes in 20 drug-naïve children and adolescents with NT1 compared with 19 healthy controls; whole-brain voxel-based morphometry, shape and volumetric analyses, and cortical thickness analysis were used. Results When compared with controls, NT1 patients revealed reduced grey matter volume in cerebellum and medial prefrontal cortex and increased volume in right hippocampus. Cortical thickness in frontal lobe was also reduced in patients compared with controls. Increased volume and shape expansion in right hippocampus in patients compared with controls were also confirmed by both vertex and volumetric analyses. Conclusions Our results indicate that subtle structural brain changes involving attentional and limbic circuits are detectable in children and adolescents with NT1. Cerebellum involvement might be related to the childhood NT1 clinical phenotype. narcolepsy, structural neuroimaging, voxel-based morphometry, shape analysis, medial prefrontal cortex, hippocampus Statement of Significance Although localized brain alterations in type 1 narcolepsy (NT1) patients have been postulated by systematic structural neuroimaging techniques, results remain controversial and potentially affected by long period of brain adaptation to the disease. The present study reveals for the first time that subtle brain changes occur early in the disease and independently of clinical course or duration; these alterations are located in key structures for attentional and limbic functions and also involve cerebellar circuits suggesting the presence of a specific clinical phenotype of NT1 in childhood. Additional studies are necessary to investigate the correlation between brain alterations and behavioural, cognitive, and motor abilities in young patients. Introduction Type 1 narcolepsy (NT1) is a lifelong disorder characterized by excessive daytime sleepiness (EDS), cataplexy, sleep paralysis, hypnagogic hallucinations, and nocturnal sleep fragmentation [1,2].It occurs in 0.025%–0.05% of the population, with peak incidence between the age of 10 and 19 years [3],although recent reports seem to indicate an earlier onset [4]. NT1 is caused by the loss of neurons that produce hypocretin in the posterolateral hypothalamus leading to low or absent hypocretin in cerebrospinal fluid and to a dysregulation of sleep and wakefulness systems [5,6]. Although the use of conventional magnetic resonance imaging (MRI) did not show consistent macroscopic lesion in NT1, systematic structural neuroimaging techniques such as voxel-based morphometry (VBM) have evidenced localized brain alterations in NT1 patients, even if with controversial results [7,8]. Several studies have found decrease of grey matter (GM) in hypothalamic structures in NT1 patients compared with healthy subjects [9–13], which is in agreement with a hypocretinergic neurotransmission dysfunction. On the contrary, other studies failed to detect hypothalamic changes but reported cortical GM decreases in frontotemporal areas [14,15], possibly related to cognitive and affective disorders typical of narcoleptic patients. Additionally, alterations were also reported in nucleus accumbens [10,11], thalamus [11,12], and mesial temporal lobe structures [16,17]. Furthermore, increases of cortical volume and thickness were demonstrated in areas of dorsolateral prefrontal cortex, suggesting compensatory processes to sustain cognitive performances in patients [18]. In conclusion, several discrepancies are evident in previous studies, both in terms of brain regions involved and direction (increases vs decreases) of volumetric changes; this heterogeneity could potentially be due to large variability in patients population, analysis methods, different disease duration and age of disease onset, and pharmacological treatment. A recent meta-analysis of VBM studies in NT1 patients failed to detect consistent results reporting no evidence of spatially consistent localized loss of GM in narcolepsy [19]. If we consider the mean age of subjects included in MRI investigations, all the above studies were performed in adult patients, therefore several years or even decades after the disease onset [4]. Indeed, because of general unawareness of this disorder and to the difficulty in diagnosis, the median time to NT1 diagnosis has been reported to be as high as 10.5 years [20] leading to a characterization of the disease after a long period of adaptation since onset. To the best of our knowledge, no neuroimaging studies have explored structural brain changes in children and adolescents with a diagnosis of NT1. It is well known that NTI in childhood and adolescence has specific features [21]. Plazzi and colleagues demonstrated the presence of complex movement disorders at disease onset that seem transient and vanish later in the course of the disease [22]. In addition, personality and behavioural changes occur early in the disease and children with NT1 have often features of depression and attention’s deficits suggesting the involvement of limbic structures. These behavioural disturbances may change during the course of the disease and its pharmacological treatment [23]. The purpose of our study was to investigate structural cortical and subcortical brain alterations in drug-naïve children and adolescents with NT1, in order to detect potential early structural brain changes at disease onset. Methods Subjects Twenty drug-naïve patients with a diagnosis of NT1 were recruited (13 males, mean age 12.15 ± 3.06 years). Patients were proposed to participate in the study if the following inclusion criteria were satisfied: (1) drug-naïve children with NT1; (2) diagnosis of NT1 according to the current criteria 2; (3) proven CSF hypocretin-1 deficiency; and (4) video documentation of cataplectic attacks during a standardized video-session [24,25] prior to the neuroimaging experimental sessions. All subjects were right-handed [26]. Table 1 reports patients’ clinical data. All patients had a complete clinical and neurological examination, conventional brain MRI, and underwent a 48-hour continuous polysomnographic recording followed by a standard five-nap Multiple Sleep Latency Test (MSLT) [27] and video recordings of cataplexy as described previously[24,25]. HLA-DQB1*0602 typing was conducted in all patients. Cataplexy frequency was assessed and subjective sleepiness was quantified by means of a modified version of the Epworth sleepiness scale (mESS) [28]. Table 1. Clinical and Laboratory Data of NT1 Patients Pt ID  Age (yr)  Gender  Disease duration (yr)  CSF hcrt  Cataplexy frequency  Hypotonia  Cataplectic facies  mESS score  MSLT-SL  MSLT-SOREMPs  1  17  F  1.6  0.0  1w–1d  no  no  15  2.6  5  2  13  F  2.0  10.7  1w–1d  no  no  13  3.30  5  3  14  M  2.8  39.8  1d  no  yes  18  5.50  5  4  11  M  3.7  43.1  1y–1m  yes  yes  13  9.00  5  5  14  F  3.2  0.0  1w–1d  no  yes  12  2.40  5  6  10  M  1.7  0.0  >1d  yes  yes  21  1.90  4  7  10  F  3.7  15.5  >1d  no  no  14  1.20  1  8  10  M  0.8  15.7  >1d  yes  yes  13  11.6  3  9  15  M  1.0  0.0  1w–1d  no  no  17  3.30  4  10  15  M  0.3  10.9  1w–1d  yes  no  16  6.40  4  11  16  M  4.5  15.5  1m–1w  yes  yes  8  5.50  3  12  11  F  1.3  0.0  1w–1d  yes  yes  14  1.00  5  13  11  M  3.3  25.0  >1d  no  no  14  9.80  4  14  9  M  1.0  35.4  1w–1d  yes  yes  9  3.40  5  15  13  M  2.7  55.0  n.r.  no  no  n.r.  13.00  4  16  8  M  0.3  0.0  1w–1d  yes  yes  10  7.50  3  17  11  F  1.2  84.2  >1d  no  no  12  8.90  3  18  8  M  1.0  0.0  >1d  yes  yes  17  4.8  2  19  7  F  0.9  0.0  >1d  yes  yes  20  9.60  1  20  16  M  6.1  0.0  1w–1d  no  no  14  2.40  5  Pt ID  Age (yr)  Gender  Disease duration (yr)  CSF hcrt  Cataplexy frequency  Hypotonia  Cataplectic facies  mESS score  MSLT-SL  MSLT-SOREMPs  1  17  F  1.6  0.0  1w–1d  no  no  15  2.6  5  2  13  F  2.0  10.7  1w–1d  no  no  13  3.30  5  3  14  M  2.8  39.8  1d  no  yes  18  5.50  5  4  11  M  3.7  43.1  1y–1m  yes  yes  13  9.00  5  5  14  F  3.2  0.0  1w–1d  no  yes  12  2.40  5  6  10  M  1.7  0.0  >1d  yes  yes  21  1.90  4  7  10  F  3.7  15.5  >1d  no  no  14  1.20  1  8  10  M  0.8  15.7  >1d  yes  yes  13  11.6  3  9  15  M  1.0  0.0  1w–1d  no  no  17  3.30  4  10  15  M  0.3  10.9  1w–1d  yes  no  16  6.40  4  11  16  M  4.5  15.5  1m–1w  yes  yes  8  5.50  3  12  11  F  1.3  0.0  1w–1d  yes  yes  14  1.00  5  13  11  M  3.3  25.0  >1d  no  no  14  9.80  4  14  9  M  1.0  35.4  1w–1d  yes  yes  9  3.40  5  15  13  M  2.7  55.0  n.r.  no  no  n.r.  13.00  4  16  8  M  0.3  0.0  1w–1d  yes  yes  10  7.50  3  17  11  F  1.2  84.2  >1d  no  no  12  8.90  3  18  8  M  1.0  0.0  >1d  yes  yes  17  4.8  2  19  7  F  0.9  0.0  >1d  yes  yes  20  9.60  1  20  16  M  6.1  0.0  1w–1d  no  no  14  2.40  5  F = female; M = male; yr = years; min = minutes; MSLT = multiple sleep latency test; SOREMPs = sleep-onset rapid eye movement periods; hcrt = hypocretin-1 level in CSF (pathological levels < 110 pg/mL); ESS = Epworth Sleepiness Scale score (range 0–24, pathological score > 10). View Large For groups’ comparisons, 19 volunteers were recruited to serve as healthy controls (HCs). Healthy subjects had no history of neurological diseases. All subjects, both patients and controls, had normal MRI at visual inspection. The human ethic committee of the University of Modena and Reggio Emilia approved this study, and written informed consent was obtained from all the patients recruited and their parents. MRI Acquisition Three-dimensional (3D) T1-weighted MRI images were acquired using a 3-T Philips Intera MRI scanner (Best, The Netherlands). A SPGR pulse sequence (echo time [TE] = 4.6 milliseconds, repetition time [TR] = 9.9 milliseconds) was used. One hundred seventy contiguous sagittal slices were acquired (voxel size = 1 × 1 × 1 mm) and field of view was 240 mm with a matrix size of 256 × 256 × 170. A T2-weighted axial scan was also acquired to allow visual determination of vascular burden or tissue abnormalities. Statistical Analysis of Demographical and Clinical Variables Demographical and clinical characteristics of the subjects were analyzed using SPSS software, and parametric and nonparametric statistics were used as appropriate. Independent samples t tests were used to compare age between patients and controls; chi-square was used to compare the two groups according to sex. VBM Analyses Structural data were analyzed with FSL-VBM (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLVBM) [29], an optimized VBM protocol [30] carried out with FSL tools (FMRIB’s Software Library, FSL: http://www.fmrib.ox.ac.uk/fsl) [31]. First, structural images were brain-extracted and GM-segmented before being registered to the MNI 152 standard space using nonlinear registration (www.fmrib.ox.ac.uk/analysis/techrep). The resulting images were averaged and flipped along the x-axis to create a left–right symmetric, study-specific GM template. Second, all native GM images were nonlinearly registered to this study-specific template and “modulated” using the Jacobian of the warp field to correct for local expansion (or contraction) due to the nonlinear component of the spatial transformation. The modulated GM images were then smoothed with an isotropic Gaussian kernel with a σ of 3 mm. Finally, voxelwise general linear modeling (GLM) was applied using permutation-based nonparametric testing (5,000 permutations); threshold-free cluster enhancement (TFCE) correction for multiple comparisons [32] was used and the statistical threshold was set at p < .05. Age, sex, and intracranial volume (calculated with SIENAX tool; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/SIENA) were entered as confounding variables in the GLM so as to control for the potential effect of these variables. In addition, separated correlation analyses with GM volume and disease duration, CSF hypocretin level, MSLT, and mESS score were performed in the patients’ group; in all analyses, age, sex, and intracranial volume were entered as regressors and a statistical threshold of p < .05 was applied. Anatomical locations of significant volume loss were determined by reference to the Harvard-Oxford cortical and subcortical structural atlas integrated into FSLview (part of FSL) in addition to careful manual inspection and identification. Shape and Volumetric Analyses Complementary to the VBM analysis, we used a different methodology to investigate neuroanatomical alterations of subcortical structures specifically. For this purpose, we used FSL/FIRST software (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIRST), a model-based segmentation/registration tool proposed by Patenaude et al 33. It uses manually labeled brain image data as a training set that was provided by the Center for Morphometric Analysis (CMA), MGH, Boston. These training data comprise of 15 subcortical structures (bilateral thalamus, caudate, putamen, pallidum, hippocampus, amygdale, accumbens, and brainstem) that were outlined by trained operators on T1-weighted MR images from 336 subjects. It contains a wide age range (4.2–72 years) and both normal and pathologic brains. The FIRST algorithm automatically segments subcortical structures based on the shape and intensity variations of the respective structure, as learnt from a training set. It uses default setting for each structure, which has been optimized empirically. Once the brain images were processed through the FSL/FIRST pipeline, each segmentation was inspected and found satisfactory. Vertex analysis (v5.0.0) was performed using first_utils in a mode of operation that aims to assess group differences on a per-vertex basis (the meshes were reconstructed in native space) [33–35]. To assess group differences, voxelwise GLM was applied using permutation-based nonparametric testing; the results adjusted for age, sex, and intracranial volume were considered significant at p < .05 cluster-based family wise error (FWE) corrected. In addition, volumetrics for the subcortical structures were derived using the scripts included in the FIRST module. To compare subcortical measures between patients and controls, we conducted a univariate ANCOVA with subcortical structure volume value as the dependent variable, group diagnosis as fixed factor, and age, gender, and intracranial volume as covariates. SPSS software (IBM, Chicago, IL) was used for statistical analyses. All neuroanatomical measures were examined for normality using Shapiro–Wilk test and transformed appropriately if they violated assumptions of normality. False discovery rate (FDR) was used to correct for multiple comparisons, and a threshold of p < .05 estimated using SPSS command, according to Bejamini and Hochberg methods [36], was considered statistically significant. Cortical Thickness Analyses Scans were analyzed using standardized image toolbox (Freesurfer, version 5.0) [37], quality assurance (outlier detection based on interquartile of 1.5 standard deviations along with visual inspection of segmentations), and statistical methods. Briefly, the pipeline involves removal of nonbrain tissue, automated Talaraich transformation, segmentation of white matter and GM, tessellation of grey or white matter boundary, automated correction of topology defects, surface deformation to form the grey or white matter boundary and grey or cerebrospinal fluid boundary, and parcellation of cerebral cortex. Cortical thickness estimates were calculated as the distance between the grey or white matter border and the pial surface at each vertex. Cortical thickness values were averaged over each major brain lobe (frontal, temporal parietal, and occipital). In addition, single value labels extracted based on automatic algorithm [38,39] were calculated for more precise analyses. Statistical analyses were performed using SPSS software (IBM, Chicago, IL). All neuroanatomical measures were examined for normality using Shapiro–Wilk test and transformed appropriately if they violated assumptions of normality. To compare cortical measures between patients and controls, we conducted a univariate ANCOVA with each neuroanatomical value as the dependent variable, group diagnosis as fixed factor, and age, gender, education, and intracranial volume as covariates. FDR was used to correct for multiple comparisons, and a threshold of p < .05 estimated using SPSS command, according to Bejamini and Hochberg methods [36], was considered statistically significant. Results Demographical and Clinical Characteristics NT1 patients (n = 20) had a mean age of 11.9 years (±2.91; range 7–17); 13 were male (Table 1). Control subjects (n = 19) had a mean age of 13.05 years (±2.17; range 6–16); 11 were male. No statistical significant difference was found in age (p = .29) and gender (p = .74) between the two groups. Mean disease duration was 2.2 years. Imaging Results Voxel-Based Morphometry Group comparison analysis between patients and controls showed that patients had reduced GM volume in right anterior cingulate or paracingulate (BA9) and cerebellum (vermis and right cerebellum lobe) and greater GM volume in right hippocampus compared with controls (Figure 1, Table 2). Table 2. Regions of GM Differences Showed by VBM Group Comparison Analyses (p < .05 TFCE Corrected) Region  t-statistic  MNI coordinates      x  y  z  Healthy controls > patients   Cerebellum, vermis  4.23  0  −62  −30   R, cerebellum, anterior lobe    22  −56  −28   R, cerebellum, posterior lobe    34  −60  −44   R, paracingulate/medial frontal gyrus (BA9)  4.40  8  56  14  Healthy controls < patients   R, hippocampus  4.91  30  −14  −20   R, hippocampal–amygdaloid transition area    24  −10  −20  Region  t-statistic  MNI coordinates      x  y  z  Healthy controls > patients   Cerebellum, vermis  4.23  0  −62  −30   R, cerebellum, anterior lobe    22  −56  −28   R, cerebellum, posterior lobe    34  −60  −44   R, paracingulate/medial frontal gyrus (BA9)  4.40  8  56  14  Healthy controls < patients   R, hippocampus  4.91  30  −14  −20   R, hippocampal–amygdaloid transition area    24  −10  −20  R = right; MNI = Montreal Neurological Institute. View Large Figure 1. View largeDownload slide VBM group comparison results. In red/yellow are depicted areas of increased volume in patients compared with controls; in blue are depicted areas of decreased volume in patients compared with controls (p < .05 TFCE corrected); R = right; coordinates in MNI = Montreal Neurological Institute. Figure 1. View largeDownload slide VBM group comparison results. In red/yellow are depicted areas of increased volume in patients compared with controls; in blue are depicted areas of decreased volume in patients compared with controls (p < .05 TFCE corrected); R = right; coordinates in MNI = Montreal Neurological Institute. Shape and Vertex Analysis Shape analysis revealed areas of significant shape expansion in the dorsolateral right hippocampus, from the head to the tail, in patients compared with the HCs (p < .05 FWE corrected) (Figure 2). No areas of hippocampal shape contraction were found in patients compared with controls. Other structures did not show any morphological change. Figure 2. View largeDownload slide Results of shape analysis for the right hippocampus. The regions in orange represent the part of the right hippocampus shown to be greater in patients compared with controls (p < .05 FWE corrected). Figure 2. View largeDownload slide Results of shape analysis for the right hippocampus. The regions in orange represent the part of the right hippocampus shown to be greater in patients compared with controls (p < .05 FWE corrected). Volumetry Volumetric analyses confirmed a significant statistically difference in right hippocampus with greater hippocampal volume in patients compared with controls (f = 5.3; p = .026). No differences were detected for other subcortical measures investigated. Correlation Analyses No correlation between GM volume and measures of disease duration, CSF hypocretin level, mESS score, and MSLT scores was detected. Cortical Thickness Analysis Considering global lobar cortical thickness analyses, we found that patients had significant reduced cortical thickness in right frontal lobe compared with controls (f = 6.8; p = .015); no differences emerged in left frontal lobe and in bilateral parietal, temporal, and occipital lobe. When considering each single label (34 for each hemisphere) and correction for multiple comparisons, no statistically significant difference remained. Discussion This is the first study investigating cortical and subcortical structural brain changes in drug-naïve children and adolescents with NT1. The possibility to measure volumetric neural changes close to disease onset gives us the opportunity to reveal early neurodevelopmental features of the disease before any anatomical and behavioural adaptation and possible drug-induced effects occur. Indeed, recent observations seem to uncover a specific clinical phenotype in children close to disease onset [22,24,25] and to suggest that functional and anatomical alterations showed in adults might be related to the severity and the duration of the disease [17,18]. In our study, we found that NT1 patients had lower GM volume in cerebellum and medial prefrontal cortex (mPFC), reduced cortical thickness in frontal lobe, and higher right hippocampus volume compared with HCs. Notably, no hypothalamic volume alteration was evident. Concerning the finding of cerebellum atrophy in NT1 patients, this is in line with the evidence that NT1 in childhood when close to disease onset frequently manifests with movement disorders [25], such as generalized hypotonia [40], possibly reflecting a modulation of orexinergic nervous system in motor control through the spinocerebellum, as demonstrated in animal experiments [41]. Only one other structural neuroimaging study reported cerebellum involvement in narcolepsy [10], but did not provide further speculation about a possible role of cerebellum in narcolepsy with cataplexy. As suggested by Pizza et al., the childhood phenotype with movement disorder decreases over time, gradually evolving into classical presentation [24]; therefore, it is quite plausible that the cerebellar structural alteration emerged in this study might be specific of this population (close to disease onset) and not detectable in older patients. Longitudinal studies with MRI comparison at different age from the disease onset should be performed to better explain this issue. The role of frontal lobe, in particular mPFC and anterior cingulate cortex (ACC) in narcolepsy, is well known and several experimental studies indicate that this region may play a critical role in narcolepsy [8,42,43], receiving excitatory projections from hypocretin neurons through the thalamic nucleus [44]. The mPFC is a critical site through which positive emotions trigger cataplexy and innervates downstream brainstem region involved in the regulation of muscle tone [45]. We recently studied the brain correlates of cataplexy in drug-naïve children or adolescent with recent onset NT1 with functional MRI (fMRI) while viewing funny videos disclosing a key activation of the ventral mPFC during cataplexy [46]. The mPFC is also involved in attentional and working memory functions and might account for attention and memory deficits emerged in neuropsychological studies in NT1 patients [47]. Moreover, anterior cingulate, as part of the limbic system, participates in awareness and emotional processing and might be related to depression and emotional instability frequently observed in NT1 patients [23]. In line with literature findings, we found a reduction of GM volume in a region including medial frontal gyrus and paracingulate cortex in NT1 patients compared with controls, suggesting that this alteration may occur early in the disease and might be related to behavioural and cognitive changes observed also in pediatric and adolescent patients. Unfortunately, in our study, we did not have any neurocognitive and personality characteristics available to provide a correlation with neuroanatomical measures. With regards to the alterations observed in right hippocampus, we found that NT1 patients had greater GM volume compared with controls and this result was confirmed with both VBM and shape analysis approach. This finding is compelling with regard to previously published morphometric studies in patients with narcolepsy showing a volume reduction in bilateral hippocampi [17], but we have to consider that our population is unique in terms of age and disease onset and a direct comparison with literature data may be difficult and misleading. The role of hippocampus in sleep–wake regulation is demonstrated by dense innervations provided by the hypocretin-containing neurons to the medial septum or diagonal band of Broca, which has been suggested to control the hippocampal theta rhythm and associated learning and memory functions through its cholinergic and GABAergic projections to the hippocampus [48,49]. Moreover, treatment with Modafinil, the most commonly prescribed central nervous system stimulant for narcolepsy, changes the cerebral metabolism and cerebral perfusion of hippocampus in narcoleptics [50], suggesting a central role of this structure in sleep–wake rhythm. Despite evidences of hippocampal involvement in narcolepsy pathophysiology, the volumetric direction of change remains challenging to explain and, without cognitive and memory measures available, we can only speculate that the greater hippocampus volume in our patients might reflect a compensatory mechanism to maintain cognitive performances, as suggested by other studies in narcolepsy [18] and other neuropsychiatric syndromes [51]. Finally, we observed a relatively asymmetric predominance of brain alteration involving mainly the right hemisphere, both in terms of lower (mPFC and cerebellum) and higher (hippocampus) GM volumetric changes. A right predominance was also detected in BOLD response during cataplexy in a recent fMRI study [46], but it was principally attributed to the type of material used to elicit laughs rather than to the neurobiology of NT1. Functional asymmetries between the two hemispheres in attentional and vigilance networks were already postulated by Posner in 1990, when he described an alerting network which focused on brain stem arousal systems along with right hemisphere related to sustained vigilance [52]. There is also evidence that the right hemisphere is selectively involved in processing negative emotions and depressive mood [53], and several studies showed that NT1 children become more introverted and manifest features of depression [47,54]. Also electrophysiological techniques provided evidence of a functional deterioration of the fronto–temporo–parietal network of the right-hemispheric vigilance system in narcolepsy and a therapeutic effect of modafinil on the left hemisphere, probably less affected by the disease [55]. The current study had limitations that deserve discussion. Firstly, the study population is numerically limited but it is selectively focused on drug-naïve and close to disease onset subjects, and this represents a unique point of view for speculations about disease pathophysiological mechanisms. Secondly, we did not collect measures of cognitive and behavioural functions in NT1 patients; therefore, we were not able to investigate correlations between personality or cognitive abilities and structural findings. Finally, as negative finding, we have not observed volumetric changes of the hypothalamus. This result can be a consequence of the inability of our morphometric techniques to detect hypothalamic changes; as a matter of fact, FIRST tool does not include hypothalamus segmentation; therefore, we were not able to perform automated shape and volumetric analysis of this structure specifically. Future studies including manual or automated specific segmentation of hypothalamus could probably better address this issue, especially if using ultra high-field MRI (7 T). In conclusion, our study demonstrated the presence of subtle anatomical brain changes in narcolepsy at disease onset involving attentional and limbic circuits, which may be specific of the disease and possibly free of any adaptation or modulation induced by disease duration and/or severity. In addition, our results support the hypothesis of a specific clinical phenotype in children characterized by neural alterations in motor systems in agreement with clinical evidence of movement disorders in NT1 children close to disease onset. Larger and possibly longitudinal studies including cognitive and behavioural measures and treatment response will be required to confirm these findings and to better specify the link between different clinical subtypes and structural abnormalities. Disclosure Statement None declared. 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