2545

Metabolic Alterations in the left dorsolateral prefrontal cortex in Sleep-Related Hypermotor Epilepsy: A Proton Magnetic Resonance Spectroscopy Study
Weina Wang1, Xiaorui Su1, Simin Zhang1, Qiang Yue2, and Qiyong Gong1

1Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China, 2Department of Radiology, West China Hospital of Sichuan University, Chengdu, China

Synopsis

Sleep-Related Hypermotor Epilepsy (SHE) is characterized by bizarre motor behavior during sleep. The aim of this present study was to investigate metabolic alterations in the bilateral DLPFC using 1H-MRS to understand the underlying pathophysiology of SHE. Thirty-nine subjects with SHE and 60 controls were studied. The left DLPFC NAA and mI concentrations of SHE patients were significantly lower than controls. There was an asymmetry of NAA in the control group. These findings may present executive function decline in SHE and further verify the left frontal lobe is more vulnerable in right-handed SHE patients.

Introduction

Sleep-related hypermotor epilepsy (SHE) is characterized by asymmetric posturing and complex hyperkinetic seizures occurred mainly in sleep.1 As a part of the central executive network, the dorsolateral prefrontal cortex (DLPFC) plays an important role in working memory and emotion.2 Sleep deprivation caused by frequently epileptic seizures exerts a negative impact on cognitive functions, including vigilance, memory retention, sensory perception and elements of executive function.3,4 Proton magnetic resonance spectroscopy (1H-MRS) is a noninvasive method to measure the brain’s neurochemical metabolites in vivo. Previous studies have demonstrated that metabolic alterations of patients with focal epilepsy. The most consistent finding mainly is the reduction of NAA in the temporal region, even rarely in extratemporal lobe.5-12 The aim of the present study was to investigate metabolic alterations, including NAA, Cho, mI, Cr, Glx in the bilateral DLPFC using 1H-MRS to better understand the underlying pathophysiology of SHE.

Methods

SHE participants were consecutively recruited from the Epilepsy Center of West China Hospital of Sichuan University. Diagnosis of SHE was confirmed by audio-video recording. All controls were recruited by poster advertisements. All participants underwent high-resolution T1-weighted MR imaging by using a Siemens 3T scanner and were MRI-negative. Single voxel 1H-MRS acquisitions were performed using Point-Resolved Echo Spectroscopy Sequence in the bilateral DLPFC (Figure 1). Spectra were analyzed using LCModel.13 We conducted analyses with General linear models (GLM) to test for interaction between age and/or gender and metabolites in SPSS Version 23. If there were, follow-up analyses were conducted to decomposed the interactions by group status, and at the 84th percentiles (older patient) and the 16th percentiles (younger patient) of age or gender effects using PROCESS Version 3.1 for SPSS.14 Otherwise, group differences were identified when age and gender were considered as covariates. Correlation analyses were performed between the metabolite concentrations and epilepsy duration, age at onset, and seizure frequency.

Results

Demographic information is provided in table 1. Thirty-nine subjects with SHE and 60 controls were studied. GLM analyses of metabolites concentrations were summarized in Table 2. As shown in Figure 2, the NAA and mI level were significantly reduced in the left DLPFC in patients compared with controls. In paired analyses, NAA concentration of the left DLPFC was statistically higher than that of the right in the control group (Table 3). The analysis on the left DLPFC Glx concentration revealed significant age-by-group interaction (p=0.038). In follow-up analyses, a negative correlation between age and left DLPFC Glx in SHE and a positive correlation in controls (Figure 3). Fisher’s R-to-Z tests demonstrated that the difference was not statistically significant (P=0.066). The PROCESS analysis showed that older patient with SHE had significantly lower Glx (P=0.017), whereas, younger patients showed higher Glx than controls (P=0.795). The Johnson-Neyman technique15 determine the age began at 33.6 years which the SHE group had significantly lower left DLPFC Glx. There were no significant correlations between metabolites and any variables in SHE patients.

Discussion

The present study has measured MRS neurometabolic features on patients with SHE, finding robustly decreased levels of NAA and mI in the left DLPFC compared with controls. The NAA content of the left DLPFC was statistically higher than that of the right in controls, but this asymmetry was disappeared in SHE patients. We hypothesize that left hemisphere of frontal lobe may be more vulnerable in SHE patients, which was related to the dominant hemisphere of patients.16 The possible explanation for mI reduction was related to cellular osmoregulation.17 A significant age-by-group interaction may be consistent with the clinical manifest that seizure frequency was much higher in younger patients, but declined in older patients in SHE patients, and the onset of seizures peaks was before the age of 20 years.18 Even though seizures may be derived from different cortical areas in SHE patients, they will propagate to frontal lobe to spell a similar seizure semiology as a final way.19 The neurometabolic alteration in the left DLPFC indicates impairment of executive function, which is under DLPFC control, presenting a specific pathophysiology of SHE.

Conlusion

The NAA and mI alterations in left DLPFC may present executive function decline in SHE and further verify the left frontal lobe is more vulnerable than the right in right-handed SHE patients.

Acknowledgements

This study was supported by the National NaturalScience Foundation (Grant Nos. 81371528, 81621003, 81761128023,81220108013, 81227002 and 81030027) and the Sichuan ProvincialFoundation of Science and Technology (Grant no. 2013SZ0047).

References

1. Tinuper P, Bisulli F, Cross JH et al. Definition and diagnostic criteria of sleep-related hypermotor epilepsy. Neurology 2016; 86: 1834-1842.

2. Seeley WW, Menon V, Schatzberg AF et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J. Neurosci. 2007; 27: 2349-2356.

3. Killgore WD. Effects of sleep deprivation on cognition. Prog. Brain Res. 2010; 185: 105-129.

4. Menghi V, Bisulli F, Tinuper P et al. Sleep-related hypermotor epilepsy: prevalence, impact and management strategies. Nat Sci Sleep 2018; 10: 317-326.

5. Cendes F, Andermann F, Dubeau F et al. Normalization of neuronal metabolic dysfunction after surgery for temporal lobe epilepsy. Evidence from proton MR spectroscopic imaging. Neurology 1997; 49: 1525-1533.

6. Hammen TKuzniecky R. Magnetic resonance spectroscopy in epilepsy. Handb. Clin. Neurol. 2012; 107: 399-408.

7. Li LM, Dubeau F, Andermann F et al. Proton magnetic resonance spectroscopic imaging studies in patients with newly diagnosed partial epilepsy. Epilepsia 2000; 41: 825-831.

8. Mueller SG, Suhy J, Laxer KD et al. Reduced extrahippocampal NAA in mesial temporal lobe epilepsy. Epilepsia 2002; 43: 1210-1216.

9. Krsek P, Hajek M, Dezortova M et al. (1)H MR spectroscopic imaging in patients with MRI-negative extratemporal epilepsy: correlation with ictal onset zone and histopathology. Eur. Radiol. 2007; 17: 2126-2135.

10. Stanley JA, Cendes F, Dubeau F et al. Proton magnetic resonance spectroscopic imaging in patients with extratemporal epilepsy. Epilepsia 1998; 39: 267-273.

11. Lundbom N, Gaily E, Vuori K et al. Proton spectroscopic imaging shows abnormalities in glial and neuronal cell pools in frontal lobe epilepsy. Epilepsia 2001; 42: 1507-1514.

12. Naldi I, Bisulli F, Testa C et al. Proton MR Spectroscopy in Patients With Sleep-Related Hypermotor Epilepsy (SHE): Evidence of Altered Cingulate Cortex Metabolism. Sleep 2017; 40.

13. Provencher SW. Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn. Reson. Med. 1993; 30: 672-679.

14. Hayes AF. Introduction to Mediation, Moderation, and Conditional Process Analysis-A Regression-Based Approach. 2nd edn.Guilford Press,2018.

15. Johnson POFay LC. The Johnson-Neyman technique, its theory and application. Psychometrika 1950; 15: 349-367.

16. Amunts K, Jancke L, Mohlberg H et al. Interhemispheric asymmetry of the human motor cortex related to handedness and gender. Neuropsychologia 2000; 38: 304-312.

17. Fisher SK, Novak JEAgranoff BW. Inositol and higher inositol phosphates in neural tissues: homeostasis, metabolism and functional significance. J. Neurochem. 2002; 82: 736-754.

18. Licchetta L, Bisulli F, Vignatelli L et al. Sleep-related hypermotor epilepsy: Long-term outcome in a large cohort. Neurology 2017; 88: 70-77.

19. Tassinari CA, Rubboli G, Gardella E et al. Central pattern generators for a common semiology in fronto-limbic seizures and in parasomnias. A neuroethologic approach. Neurol. Sci. 2005; 26 Suppl 3: s225-232.

Figures

Figure 1. Location of the voxels in the bilateral DLPFC. The proportion of CSF within the voxels were determined by overlaying the voxels on the segmented T1-weighted images according to its location information.

Figure 2. Differences in NAA, mI concentrations in the left DLPFC in patients with SHE compared to controls. Asterisk indicates a significant difference between SHE patients and controls (P < 0.05).

Figure 3. Scatterplot of age and left DLPFC Glx level showing regression lines by group status.

Table 1

Table 2 and 3

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
2545