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Altered spontaneous activity in treatment-naive childhood absence epilepsy revealed by Regional Homogeneity
Journal of the Neurological Sciences 340 (2014) 58–62
Contents lists available at ScienceDirect
Journal of the Neurological Sciences journal homepage: www.elsevier.com/locate/jns
Altered spontaneous activity in treatment-naive childhood absence epilepsy revealed by Regional Homogeneity☆ Tianhua Yang a,1, Zhijia Fang b,1, Jiechuan Ren a, Fenglai Xiao a, Qifu Li c, Ling Liu a, Du lei b, Qiyong Gong b,⁎, Dong Zhou a,⁎⁎ a b c
Department of Neurology, West China Hospital, Sichuan University, Cheng du, PR China Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, PR China Department of Neurology, The Affiliated Hospital of Hainan Medical College, Haikou, PR China
a r t i c l e
i n f o
Article history: Received 29 November 2013 Received in revised form 17 February 2014 Accepted 21 February 2014 Available online 28 February 2014 Keywords: fMRI CAE Regional homogeneity Resting state Synchronization Default mode network
a b s t r a c t Purpose: To explore the differences in regional spontaneous activities throughout the whole brain by the Regional Homogeneity (ReHo) method in untreated childhood absence epilepsy (CAE), in order to understand the neuropathophysiological mechanism of function impairments in CAE. Methods: The rest-functional MRI was used to measure the ReHo in 16 patients with untreated CAE and 16 ageand sex-matched healthy controls. The correlations between the ReHo at each voxel of the whole brain and duration of epilepsy were analyzed. Results: Compared with healthy controls, we found that ReHo was decreased in bilateral thalamus, caudate, posterior lobe of cerebellum and areas mainly in the default mode network (DMN) (including precuneus and posterior cingulate cortex-PCC, bilateral inferior lateral parietal lobule). The increase of ReHo was found in bilateral insula, left occipital cortex. Moreover, a correlation analysis of the ReHo measurement versus the epilepsy duration was performed, and highly positive correlation was observed in precuneus/PCC and supplementary motor area (SMA). Significance: The current findings demonstrated alterations of ReHo in the striato-thalamo-cortical network in drug naïve CAE subjects during interictal resting state. Some regions with decreased ReHo followed the pattern of ‘default’ state of brain function. In addition, positive correlations between the ReHo values in the precuneus/PCC and SMA and the disease duration were identified. These results indicate that the involvement of these regions may be related to the pathomechanisms of seizure generation and the neurological deficits observed in CAE patients. ReHo has demonstrated the capability to characterize spontaneous brain dysfunction in epilepsy. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Childhood absence epilepsy (CAE) is a subtype of idiopathic generalized epilepsy (IGE), which is the most common childhood epilepsy syndrome with a broad range of cognitive, linguistic, and behavioral/ emotional comorbidities [1]. Previous study has found volume changes in frontal and temporal lobes as well as the amygdala in CAE subjects through voxel-based morphometry [2,3] and white matter integrity
☆ Disclosure: The authors report no conflicts of interests. The work was performed in Sichuan University. ⁎ Correspondence to: Q. Gong, HMRRC in West China Hospital, Sichuan University, 610041 Chengdu, Sichuan, PR China. ⁎⁎ Correspondence to: D. Zhou, Department of Neurology in West China Hospital, Sichuan University, 610041 Chengdu, Sichuan, PR China. E-mail addresses: [email protected] (Q. Gong), [email protected] (D. Zhou). 1 The co-first author.
http://dx.doi.org/10.1016/j.jns.2014.02.025 0022-510X/© 2014 Elsevier B.V. All rights reserved.
abnormality through diffusion tensor imaging [4]. In addition, neuronal dysfunction in the thalami was also demonstrated in CAE by magnetic resonance spectroscopy [5]. Resting functional connectivity has shown the abnormal connectivity in different networks [6,7] and between the hemispheres [8] in patients with CAE. Resting-state functional magnetic resonance imaging reflects spontaneous brain activities of the human brain. Based on that, different from the traditional seed-voxel approach, a novel data-driven method called Regional Homogeneity (ReHo) was developed to measure the synchronization of activity in different brain regions [9]. This method has been used to investigate healthy subjects and neurological diseases such as Alzheimer's disease [10], Parkinson's disease [11], schizophrenia [12], depression [13], and attention-deficit/hyperactivity disorder [14]. In addition, this method has also been used to evaluate ReHo in epilepsy patients with generalized tonic–clonic seizures [15] and temporal lobe epilepsy [16,17]. However, to the best of our knowledge, until now, no study has observed the alteration of synchrony in drug naïve CAE patients using the ReHo method.
T. Yang et al. / Journal of the Neurological Sciences 340 (2014) 58–62
Therefore, we hypothesized that the ReHo index would be different between patients with CAE and normal controls. In the current study, we applied ReHo method to a homogeneous group of newly diagnosed and untreated children with CAE to determine whether the ReHo would change in CAE compared to healthy controls. We also investigated the influence of clinical factor–epilepsy duration on ReHo in CAE. 2. Material and methods 2.1. Patients Diagnosis was established according to the diagnostic scheme published by the International League Against Epilepsy in 2001 [18]. All the patients were newly diagnosed and had not received anti-epileptic medications. The patients with CAE were recruited from the epilepsy clinics at the West China Hospital of Sichuan University. All patients underwent routine clinical neuroimaging and EEG monitoring to ensure that there were no structural abnormalities. Patients with self-reported mental disorders or cognitive handicaps were excluded. In addition, the 16 age- and sex-matched healthy controls were recruited also. This study was approved by a responsible governmental agency at the Sichuan University. Informed consent for the study was obtained from each subject. 2.2. Data acquisition The fMRI data were acquired with a 3.0 T Siemens Tim Trio wholebody MRI system (Siemens Medical Solutions, Erlangen, Germany). A structural scan was acquired for the localization of functional scans. Then, the functional images were recorded axially using an echo-planar imaging (EPI) sequence with the following parameters: 30 slices, 205 volumes, TR = 2000 ms, TE = 30 ms, FOV = 24 cm, matrix = 64 × 64, in-plane resolution = 22 mm, and a flip angle = 90. The structural image was also acquired (TR = 20 ms, TE = 3.69 ms, FOV =
59
25 cm × 25 cm, flip angle = 12°,matrix = 320 × 320,128 slices). During the resting state scans, a standard birdcage head coil was used together with a restraining foam pad to minimize head motion and diminish scanner noise. In addition, the subjects were instructed to keep their eyes closed, remain still, and not think of anything.
2.3. Data preprocess analysis, seed defined and seed correlation analysis Pre-processing of fMRI data was conducted using the SPM8 software package (statistical parametric mapping http://www.fil.ion.ucl.ac.uk/ spm). The slice time correction, 3D motion detection and correction, spatial normalization to the Montreal Neurological Institute (MNI) template supplied by SPM8, and spatial smoothing using an isotropic Gaussian kernel (4 mm full width at half maximum) were included. Several procedures were used to remove the possible variances from time course of each voxel. Temporal band-pass filtering (pass band 0.01–0.08 Hz) was conducted through a phase-insensitive filtering. After that, the time series were further corrected through linear regression to eliminate the effect of six head motion parameters obtained in the realigning step and the effect of the signals from a CSF region and a white matter region. The residuals of the regressions were linearly detrended, and then used for the next analysis. Further data preprocessing and ReHo analysis was performed with REST software [19,20] (http://resting-fmri.sourceforge.net). A filtered time series was transformed to the frequency domain with a fast Fourier transform; and then ReHo was obtained by calculating the square root of the power spectrum in the frequency range of 0.01–0.08 Hz [21]. For standardization, the ReHo of each voxel was further divided by the global mean of ReHo values. To observe where in the brain the standardized Kendall coefficient of concordance value was larger than one in each group, one-sided and one-sample t-tests were performed to generate the T maps in both patients and healthy control. The significant threshold was set at P b 0.001 (AlphaSim corrected implementation based on the Monte Carlo simulation in AFNI in REST [19]). To examine
Fig. 1. ReHo results for CAE patients and the healthy control group. The ReHo map appeared to be similar during visual inspection of the two groups. A: the healthy control group; B: CAE patients. The significant threshold was set at P b 0.001 (AlphaSim-corrected).
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T. Yang et al. / Journal of the Neurological Sciences 340 (2014) 58–62
the difference between groups, two-sample t-test in REST was used. Significance level was set at p b 0.05, corrected for comparisons using the “AlphaSim”. To identify the association between ReHo and the illness duration, the Pearson correlation was calculated at each voxel within a mask obtained from one-sample t-test results on the CAE group. For the correlation analysis, a threshold of P b 0.05, using the AlphaSim program in the REST software, was chosen as a threshold for statistical significance.
3. Results 3.1. Clinical features Sixteen patients diagnosed with CAE were recruited (5 males, 11 females, mean age 9.2, range 7–12). None of them has received antiepileptic medications. Sixteen sessions of fMRI scanning in 16 patients were obtained.
3.2. Between groups ReHo results for the healthy control group and CAE patients were shown respectively in A and B of Fig. 1. The ReHo map appeared to be similar during visual inspection of the two groups. Significantly increased ReHo was found mainly in the default mode network (DMN) (including precuneus, posterior cingulate cortex-PCC, bilateral inferior lateral parietal lobule, and medial prefrontal cortex). In addition, we also found that other brain regions, including the bilateral thalamus, putamen, caudate, insula, dorsolateral prefrontal cortex, anterior cingulate gyrus, bilateral visual cortex, and cerebellum, have higher ReHo values. A two-sample t-test was performed to examine the differences between patients with CAE and healthy controls. Compared with healthy controls, we found that ReHo was decreased in bilateral thalamus, caudate, posterior lobe of cerebellum and areas mainly in the DMN regions including precuneus, PCC, and bilateral inferior lateral parietal lobule. The increase of ReHo was found in bilateral insula and left occipital cortex (shown in Fig. 2).
Fig. 2. Brain regions showing ReHo difference between the CAE patients and healthy controls. Red and blue denote higher and lower ReHo respectively and the color bars indicate the T value. The statistical threshold was P b 0.05 (AlphaSim-corrected).
T. Yang et al. / Journal of the Neurological Sciences 340 (2014) 58–62
Fig. 3. Results of the correlation analysis between the epilepsy duration and ReHo in CAE patients (P b 0.05, AlphaSim-corrected). Analysis by regressing ReHo at each voxel in the whole brain with the epilepsy duration in the CAE revealed that the most significant correlation occurs bilaterally in precuneus/PCC and SMA.
3.3. Correlations between ReHo and epilepsy duration This analysis determined which regions manifested with significant relationships between the ReHo and the epilepsy duration. Analysis by regressing ReHo at each voxel in the whole brain with the epilepsy duration in the CAE revealed that the most significant correlation occurs bilaterally in precuneus/PCC and supplementary motor area (SMA) (shown in Fig. 3). 4. Discussion This study demonstrated the alterations of regional synchronization in drug naïve CAE patients during interictal resting state through ReHo analysis. CAE patients exhibited ReHo changes in the striato-thalamocortical network compared to healthy controls. What is more, the data support the existence of positive correlation between the duration of the disease and ReHo bilaterally in precuneus/PCC and SMA. These results indicate that the involvement of these regions in specific cortical and subcortical networks may be related to the pathomechanisms of seizure generation and the neurological deficits observed in CAE patients. Previous studies have found thalamus abnormalities in CAE patients including bilateral thalamic atrophy [22–24] and neuronal metabolic dysfunction [5]. Our previous work has found that untreated CAE patients had a significantly higher fractional anisotropy value in the bilateral thalamus [4]. Most studies investigated the generalized spike-wave discharges (GSWDs) related to blood oxygenation level dependent (BOLD) change and found bilateral BOLD signal increase in thalamus during GSWDs [25,26]. Recently, decreased connectivity in the thalamus and basal ganglia and increased connectivity in the medial occipital cortex were also found [8]. In this study, we found reduced ReHo in thalamus during interictal resting state and this reflects that thalamic function is also abnormal in the “baseline” state in CAE. Resting state basal ganglia network abnormality has been found in idiopathic generalized epilepsy (IGE) [27]. GSWDs were associated with symmetrical deactivation of caudate nucleus during ictal GSWDs [28]. Another study found that a BOLD signal decrease in the head of the caudate nucleus occurred before GSWDs [29]. In consistent with
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these results, we found reduced ReHo in bilateral caudate. And in line with this, the caudate-putamen showed decreased cerebral blood flow during normal whisker stimulation in an established animal model of absence epilepsy [30]. Cerebellum is involved in IGE, and a clear activation was seen in the cerebellum in response to epileptic discharges [31,32]. Our previous study showed increased mean diffusivity values in bilateral cerebellar hemispheres in CAE [4]. On the other hand, the involvement of the cerebellum in emotional and cognitive processing has been found in many studies [33]. Our findings, in line with the above studies, found that ReHo was reduced in bilateral posterior lobe of cerebellum. This indicates that CAE patients had abnormal spontaneous neural activity in the cerebellum, which might partially underlie the neurological deficits of CAE. The insula is a functional complex region of the cortex, involved in processing affect, sensory-motor processing, and general cognition [34]. Gray matter volume decrease [35] and increased activity bilaterally in the insula have been found in IGE [36,37]. In line with these reports, we also found the increase of ReHo in bilateral insula. ReHo increase in insula may reflect compensatory increased spontaneous activity in CAE patients. In addition, we found ReHo increase in left occipital cortex. In another report, it has been found occipital activation preceded the onset of GSWDs [38], and functional connectivity of the occipital cortex was increased to primary visual, auditory and sensorimotor cortical regions [39]. These findings suggest that the occipital cortex may play an important role in absence epilepsy that warrants further investigation. In our study, decreased ReHo was mainly in precuneus, PCC, and inferior parietal lobule. These brain regions overlap with the components of DMN that is involved in the integration of cognitive and emotional processing and monitoring the world around us. ReHo decrease in these areas indicates DMN abnormalities in CAE and this is also supported by other reports [6]. What is more, positive correlations between the ReHo values in the precuneus/PCC and SMA and the disease duration were identified. These results suggest that these regions may have a role in the chronicity of CAE. The precuneus/PCC which has been revealed by group comparison is the hubs of DMN network. Although the correlation in SMA was not within the region in which patients showed deficits in ReHo, SMA has been considered to play an important role in linking cognition to action [40]. These results indicate that along with disease duration, ReHo increase in these areas may reflect compensatory increased spontaneous activity for the disrupted global cognitive function in patients. Some other issues should be mentioned. First, the sample size is modest; it is possible that larger sample size will provide further insights. However, it should be noted that our group was a homogeneous group of patients without medication. Second, the cardiac and respiratory signals were not recorded. Third, simultaneous EEG during the fMRI scanning was not performed here, and it is impossible to rule out the confounding epileptic discharges during MR scanning. Fourth, we cannot exclude the possibility that the group differences partly resulted from spontaneous thoughts and internal cognitive processing during the scanning.
5. Conclusions The current findings demonstrated alterations of ReHo in the striatothalamo-cortical network in drug naïve CAE subjects during interictal resting state. Some regions with decreased ReHo followed the pattern of ‘default’ state of brain function. In addition, positive correlations between the ReHo values in the precuneus/PCC and SMA and the disease duration were identified. These results indicate that the involvement of these regions may be related to the pathomechanisms of seizure generation and the neurological deficits observed in CAE patients. ReHo has demonstrated the capability to characterize spontaneous brain dysfunction in epilepsy.
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Disclosure of conflicts of interest None of the authors has any conflict of interest to disclose. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines. Acknowledgments This work was supported by the National Natural Science Foundation of China (Contract grant number: 30811120424, 81100974/H091, 30870655 and 81071222), grant from the Sichuan University Research Fund (No: 2010SCU11034), grant form China Postdoctoral Science Foundation (No: 20100481388 and No: 201104692), the 973 Project 2011CB707803, and the 111 project. The authors would like to acknowledge all participants in the study. References [1] Caplan R, Siddarth P, Stahl L, Lanphier E, Vona P, Gurbani S, et al. Childhood absence epilepsy: behavioral, cognitive, and linguistic comorbidities. Epilepsia 2008;49:1838–46. [2] Caplan R, Levitt J, Siddarth P, Wu KN, Gurbani S, Sankar R, et al. Frontal and temporal volumes in childhood absence epilepsy. Epilepsia 2009;50:2466–72. [3] Cohen AS, Daley M, Siddarth P, Levitt J, Loesch IK, Altshuler L, et al. Amygdala volumes in childhood absence epilepsy. Epilepsy Behav 2009;16:436–41. [4] Yang T, Guo Z, Luo C, Li Q, Yan B, Liu L, et al. White matter impairment in the basal ganglia-thalamocortical circuit of drug-naive childhood absence epilepsy. Epilepsy Res 2012;99:267–73. [5] Fojtikova D, Brazdil M, Horky J, Mikl M, Kuba R, Krupa P, et al. Magnetic resonance spectroscopy of the thalamus in patients with typical absence epilepsy. SeizureEur J Epilep 2006;15:533–40. [6] Yang T, Luo C, Li Q, Guo Z, Liu L, Gong Q, et al. Altered resting-state connectivity during interictal generalized spike-wave discharges in drug-naive childhood absence epilepsy. Hum Brain Mapp 2013;34:1761–7. [7] Killory BD, Bai X, Negishi M, Vega C, Spann MN, Vestal M, et al. Impaired attention and network connectivity in childhood absence epilepsy. Neuroimage 2011;56:2209–17. [8] Masterton RA, Carney PW, Jackson GD. Cortical and thalamic resting-state functional connectivity is altered in childhood absence epilepsy. Epilepsy Res 2012;99:327–34. [9] Zang Y, Jiang T, Lu Y, He Y, Tian L. Regional homogeneity approach to fMRI data analysis. Neuroimage 2004;22:394–400. [10] Zhang Z, Liu Y, Jiang T, Zhou B, An N, Dai H, et al. Altered spontaneous activity in Alzheimer's disease and mild cognitive impairment revealed by Regional Homogeneity. Neuroimage 2012;59:1429–40. [11] Wu T, Long X, Zang Y, Wang L, Hallett M, Li K, et al. Regional homogeneity changes in patients with Parkinson's disease. Hum Brain Mapp 2009;30:1502–10. [12] Alexander-Bloch AF, Gogtay N, Meunier D, Birn R, Clasen L, Lalonde F, et al. Disrupted modularity and local connectivity of brain functional networks in childhood-onset schizophrenia. Front Syst Neurosci 2010;4:147. [13] Wu QZ, Li DM, Kuang WH, Zhang TJ, Lui S, Huang XQ, et al. Abnormal regional spontaneous neural activity in treatment-refractory depression revealed by resting-state fMRI. Hum Brain Mapp 2011;32:1290–9. [14] Zhu CZ, Zang YF, Liang M, Tian LX, He Y, Li XB, et al. Discriminative analysis of brain function at resting-state for attention-deficit/hyperactivity disorder. Med Image Comput Comput Assist Interv 2005;8:468–75. [15] Zhong Y, Lu G, Zhang Z, Jiao Q, Li K, Liu Y. Altered regional synchronization in epileptic patients with generalized tonic–clonic seizures. Epilepsy Res 2011;97:83–91. [16] Mankinen K, Long XY, Paakki JJ, Harila M, Rytky S, Tervonen O, et al. Alterations in regional homogeneity of baseline brain activity in pediatric temporal lobe epilepsy. Brain Res 2011;1373:221–9.
[17] Zeng H, Pizarro R, Nair VA, La C, Prabhakaran V. Alterations in regional homogeneity of resting-state brain activity in mesial temporal lobe epilepsy. Epilepsia 2013; 54:658–66. [18] Engel J. A proposed diagnostic scheme for people with epileptic seizures and with epilepsy. Report of the ILAE task force on classification and terminology. Akt Neurol 2001:28–305 [−+]. [19] Song XW, Dong ZY, Long XY, Li SF, Zuo XN, Zhu CZ, et al. REST: a toolkit for restingstate functional magnetic resonance imaging data processing. PLoS One 2011;6: e25031. [20] Chao-Gan Y, Yu-Feng Z. DPARSF: a MATLAB toolbox for “Pipeline” data analysis of resting-state fMRI. Front Syst Neurosci 2010;4:13. [21] Zang YF, He Y, Zhu CZ, Cao QJ, Sui MQ, Liang M, et al. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev 2007;29:83–91. [22] Pardoe H, Pell GS, Abbott DF, Berg AT, Jackson GD. Multi-site voxel-based morphometry: methods and a feasibility demonstration with childhood absence epilepsy. Neuroimage 2008;42:611–6. [23] Chan CH, Briellmann RS, Pell GS, Scheffer IE, Abbott DF, Jackson GD. Thalamic atrophy in childhood absence epilepsy. Epilepsia 2006;47:399–405. [24] Bernhardt BC, Rozen DA, Worsley KJ, Evans AC, Bernasconi N, Bernasconi A. Thalamo-cortical network pathology in idiopathic generalized epilepsy: insights from MRI-based morphometric correlation analysis. Neuroimage 2009;46:373–81. [25] Moeller F, Siebner HR, Wolff S, Muhle H, Granert O, Jansen O, et al. Simultaneous EEG-fMRI in drug-naive children with newly diagnosed absence epilepsy. Epilepsia 2008;49:1510–9. [26] Labate A, Briellmann RS, Abbott DF, Waites AB, Jackson GD. Typical childhood absence seizures are associated with thalamic activation. Epileptic Disord 2005;7:373–7. [27] Luo C, Li Q, Xia Y, Lei X, Xue K, Yao Z, et al. Resting state basal ganglia network in idiopathic generalized epilepsy. Hum Brain Mapp 2012;33:1279–94. [28] Li Q, Luo C, Yang T, Yao Z, He L, Liu L, et al. EEG-fMRI study on the interictal and ictal generalized spike-wave discharges in patients with childhood absence epilepsy. Epilepsy Res 2009;87:160–8. [29] Moeller F, Siebner HR, Wolff S, Muhle H, Boor R, Granert O, et al. Changes in activity of striato-thalamo-cortical network precede generalized spike wave discharges. Neuroimage 2008;39:1839–49. [30] Mishra AM, Ellens DJ, Schridde U, Motelow JE, Purcaro MJ, DeSalvo MN, et al. Where fMRI and electrophysiology agree to disagree: corticothalamic and striatal activity patterns in the WAG/Rij rat. J Neurosci 2011;31:15053–64. [31] Moeller F, Maneshi M, Pittau F, Gholipour T, Bellec P, Dubeau F, et al. Functional connectivity in patients with idiopathic generalized epilepsy. Epilepsia 2011;52:515–22. [32] Gotman J, Grova C, Bagshaw A, Kobayashi E, Aghakhani Y, Dubeau F. Generalized epileptic discharges show thalamocortical activation and suspension of the default state of the brain. Proc Natl Acad Sci U S A 2005;102:15236–40. [33] Salmi J, Pallesen KJ, Neuvonen T, Brattico E, Korvenoja A, Salonen O, et al. Cognitive and motor loops of the human cerebro-cerebellar system. J Cogn Neurosci 2010; 22:2663–76. [34] Menon V, Uddin LQ. Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct 2010;214:655–67. [35] Huang W, Lu G, Zhang Z, Zhong Y, Wang Z, Yuan C, et al. Gray-matter volume reduction in the thalamus and frontal lobe in epileptic patients with generalized tonic– clonic seizures. J Neuroradiol 2011;38:298–303. [36] Clemens B, Bessenyei M, Piros P, Toth M, Seress L, Kondakor I. Characteristic distribution of interictal brain electrical activity in idiopathic generalized epilepsy. Epilepsia 2007;48:941–9. [37] Liu YH, Yang TH, Liao W, Yang XH, Liu I, Yan B, et al. EEG-fMRI study of the ictal and interictal epileptic activity in patients with eyelid myoclonia with absences. Epilepsia 2008;49:2078–86. [38] Bai X, Vestal M, Berman R, Negishi M, Spann M, Vega C, et al. Dynamic time course of typical childhood absence seizures: EEG, behavior, and functional magnetic resonance imaging. J Neurosci 2010;30:5884–93. [39] Masterton RA, Carney PW, Jackson GD. Cortical and thalamic resting-state functional connectivity is altered in childhood absence epilepsy. Epilepsy Res 2012;99:327–34. [40] Schall JD, Stuphorn V, Brown JW. Monitoring and control of action by the frontal lobes. Neuron 2002;36:309–22.
Contents lists available at ScienceDirect
Journal of the Neurological Sciences journal homepage: www.elsevier.com/locate/jns
Altered spontaneous activity in treatment-naive childhood absence epilepsy revealed by Regional Homogeneity☆ Tianhua Yang a,1, Zhijia Fang b,1, Jiechuan Ren a, Fenglai Xiao a, Qifu Li c, Ling Liu a, Du lei b, Qiyong Gong b,⁎, Dong Zhou a,⁎⁎ a b c
Department of Neurology, West China Hospital, Sichuan University, Cheng du, PR China Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, PR China Department of Neurology, The Affiliated Hospital of Hainan Medical College, Haikou, PR China
a r t i c l e
i n f o
Article history: Received 29 November 2013 Received in revised form 17 February 2014 Accepted 21 February 2014 Available online 28 February 2014 Keywords: fMRI CAE Regional homogeneity Resting state Synchronization Default mode network
a b s t r a c t Purpose: To explore the differences in regional spontaneous activities throughout the whole brain by the Regional Homogeneity (ReHo) method in untreated childhood absence epilepsy (CAE), in order to understand the neuropathophysiological mechanism of function impairments in CAE. Methods: The rest-functional MRI was used to measure the ReHo in 16 patients with untreated CAE and 16 ageand sex-matched healthy controls. The correlations between the ReHo at each voxel of the whole brain and duration of epilepsy were analyzed. Results: Compared with healthy controls, we found that ReHo was decreased in bilateral thalamus, caudate, posterior lobe of cerebellum and areas mainly in the default mode network (DMN) (including precuneus and posterior cingulate cortex-PCC, bilateral inferior lateral parietal lobule). The increase of ReHo was found in bilateral insula, left occipital cortex. Moreover, a correlation analysis of the ReHo measurement versus the epilepsy duration was performed, and highly positive correlation was observed in precuneus/PCC and supplementary motor area (SMA). Significance: The current findings demonstrated alterations of ReHo in the striato-thalamo-cortical network in drug naïve CAE subjects during interictal resting state. Some regions with decreased ReHo followed the pattern of ‘default’ state of brain function. In addition, positive correlations between the ReHo values in the precuneus/PCC and SMA and the disease duration were identified. These results indicate that the involvement of these regions may be related to the pathomechanisms of seizure generation and the neurological deficits observed in CAE patients. ReHo has demonstrated the capability to characterize spontaneous brain dysfunction in epilepsy. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Childhood absence epilepsy (CAE) is a subtype of idiopathic generalized epilepsy (IGE), which is the most common childhood epilepsy syndrome with a broad range of cognitive, linguistic, and behavioral/ emotional comorbidities [1]. Previous study has found volume changes in frontal and temporal lobes as well as the amygdala in CAE subjects through voxel-based morphometry [2,3] and white matter integrity
☆ Disclosure: The authors report no conflicts of interests. The work was performed in Sichuan University. ⁎ Correspondence to: Q. Gong, HMRRC in West China Hospital, Sichuan University, 610041 Chengdu, Sichuan, PR China. ⁎⁎ Correspondence to: D. Zhou, Department of Neurology in West China Hospital, Sichuan University, 610041 Chengdu, Sichuan, PR China. E-mail addresses: [email protected] (Q. Gong), [email protected] (D. Zhou). 1 The co-first author.
http://dx.doi.org/10.1016/j.jns.2014.02.025 0022-510X/© 2014 Elsevier B.V. All rights reserved.
abnormality through diffusion tensor imaging [4]. In addition, neuronal dysfunction in the thalami was also demonstrated in CAE by magnetic resonance spectroscopy [5]. Resting functional connectivity has shown the abnormal connectivity in different networks [6,7] and between the hemispheres [8] in patients with CAE. Resting-state functional magnetic resonance imaging reflects spontaneous brain activities of the human brain. Based on that, different from the traditional seed-voxel approach, a novel data-driven method called Regional Homogeneity (ReHo) was developed to measure the synchronization of activity in different brain regions [9]. This method has been used to investigate healthy subjects and neurological diseases such as Alzheimer's disease [10], Parkinson's disease [11], schizophrenia [12], depression [13], and attention-deficit/hyperactivity disorder [14]. In addition, this method has also been used to evaluate ReHo in epilepsy patients with generalized tonic–clonic seizures [15] and temporal lobe epilepsy [16,17]. However, to the best of our knowledge, until now, no study has observed the alteration of synchrony in drug naïve CAE patients using the ReHo method.
T. Yang et al. / Journal of the Neurological Sciences 340 (2014) 58–62
Therefore, we hypothesized that the ReHo index would be different between patients with CAE and normal controls. In the current study, we applied ReHo method to a homogeneous group of newly diagnosed and untreated children with CAE to determine whether the ReHo would change in CAE compared to healthy controls. We also investigated the influence of clinical factor–epilepsy duration on ReHo in CAE. 2. Material and methods 2.1. Patients Diagnosis was established according to the diagnostic scheme published by the International League Against Epilepsy in 2001 [18]. All the patients were newly diagnosed and had not received anti-epileptic medications. The patients with CAE were recruited from the epilepsy clinics at the West China Hospital of Sichuan University. All patients underwent routine clinical neuroimaging and EEG monitoring to ensure that there were no structural abnormalities. Patients with self-reported mental disorders or cognitive handicaps were excluded. In addition, the 16 age- and sex-matched healthy controls were recruited also. This study was approved by a responsible governmental agency at the Sichuan University. Informed consent for the study was obtained from each subject. 2.2. Data acquisition The fMRI data were acquired with a 3.0 T Siemens Tim Trio wholebody MRI system (Siemens Medical Solutions, Erlangen, Germany). A structural scan was acquired for the localization of functional scans. Then, the functional images were recorded axially using an echo-planar imaging (EPI) sequence with the following parameters: 30 slices, 205 volumes, TR = 2000 ms, TE = 30 ms, FOV = 24 cm, matrix = 64 × 64, in-plane resolution = 22 mm, and a flip angle = 90. The structural image was also acquired (TR = 20 ms, TE = 3.69 ms, FOV =
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25 cm × 25 cm, flip angle = 12°,matrix = 320 × 320,128 slices). During the resting state scans, a standard birdcage head coil was used together with a restraining foam pad to minimize head motion and diminish scanner noise. In addition, the subjects were instructed to keep their eyes closed, remain still, and not think of anything.
2.3. Data preprocess analysis, seed defined and seed correlation analysis Pre-processing of fMRI data was conducted using the SPM8 software package (statistical parametric mapping http://www.fil.ion.ucl.ac.uk/ spm). The slice time correction, 3D motion detection and correction, spatial normalization to the Montreal Neurological Institute (MNI) template supplied by SPM8, and spatial smoothing using an isotropic Gaussian kernel (4 mm full width at half maximum) were included. Several procedures were used to remove the possible variances from time course of each voxel. Temporal band-pass filtering (pass band 0.01–0.08 Hz) was conducted through a phase-insensitive filtering. After that, the time series were further corrected through linear regression to eliminate the effect of six head motion parameters obtained in the realigning step and the effect of the signals from a CSF region and a white matter region. The residuals of the regressions were linearly detrended, and then used for the next analysis. Further data preprocessing and ReHo analysis was performed with REST software [19,20] (http://resting-fmri.sourceforge.net). A filtered time series was transformed to the frequency domain with a fast Fourier transform; and then ReHo was obtained by calculating the square root of the power spectrum in the frequency range of 0.01–0.08 Hz [21]. For standardization, the ReHo of each voxel was further divided by the global mean of ReHo values. To observe where in the brain the standardized Kendall coefficient of concordance value was larger than one in each group, one-sided and one-sample t-tests were performed to generate the T maps in both patients and healthy control. The significant threshold was set at P b 0.001 (AlphaSim corrected implementation based on the Monte Carlo simulation in AFNI in REST [19]). To examine
Fig. 1. ReHo results for CAE patients and the healthy control group. The ReHo map appeared to be similar during visual inspection of the two groups. A: the healthy control group; B: CAE patients. The significant threshold was set at P b 0.001 (AlphaSim-corrected).
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the difference between groups, two-sample t-test in REST was used. Significance level was set at p b 0.05, corrected for comparisons using the “AlphaSim”. To identify the association between ReHo and the illness duration, the Pearson correlation was calculated at each voxel within a mask obtained from one-sample t-test results on the CAE group. For the correlation analysis, a threshold of P b 0.05, using the AlphaSim program in the REST software, was chosen as a threshold for statistical significance.
3. Results 3.1. Clinical features Sixteen patients diagnosed with CAE were recruited (5 males, 11 females, mean age 9.2, range 7–12). None of them has received antiepileptic medications. Sixteen sessions of fMRI scanning in 16 patients were obtained.
3.2. Between groups ReHo results for the healthy control group and CAE patients were shown respectively in A and B of Fig. 1. The ReHo map appeared to be similar during visual inspection of the two groups. Significantly increased ReHo was found mainly in the default mode network (DMN) (including precuneus, posterior cingulate cortex-PCC, bilateral inferior lateral parietal lobule, and medial prefrontal cortex). In addition, we also found that other brain regions, including the bilateral thalamus, putamen, caudate, insula, dorsolateral prefrontal cortex, anterior cingulate gyrus, bilateral visual cortex, and cerebellum, have higher ReHo values. A two-sample t-test was performed to examine the differences between patients with CAE and healthy controls. Compared with healthy controls, we found that ReHo was decreased in bilateral thalamus, caudate, posterior lobe of cerebellum and areas mainly in the DMN regions including precuneus, PCC, and bilateral inferior lateral parietal lobule. The increase of ReHo was found in bilateral insula and left occipital cortex (shown in Fig. 2).
Fig. 2. Brain regions showing ReHo difference between the CAE patients and healthy controls. Red and blue denote higher and lower ReHo respectively and the color bars indicate the T value. The statistical threshold was P b 0.05 (AlphaSim-corrected).
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Fig. 3. Results of the correlation analysis between the epilepsy duration and ReHo in CAE patients (P b 0.05, AlphaSim-corrected). Analysis by regressing ReHo at each voxel in the whole brain with the epilepsy duration in the CAE revealed that the most significant correlation occurs bilaterally in precuneus/PCC and SMA.
3.3. Correlations between ReHo and epilepsy duration This analysis determined which regions manifested with significant relationships between the ReHo and the epilepsy duration. Analysis by regressing ReHo at each voxel in the whole brain with the epilepsy duration in the CAE revealed that the most significant correlation occurs bilaterally in precuneus/PCC and supplementary motor area (SMA) (shown in Fig. 3). 4. Discussion This study demonstrated the alterations of regional synchronization in drug naïve CAE patients during interictal resting state through ReHo analysis. CAE patients exhibited ReHo changes in the striato-thalamocortical network compared to healthy controls. What is more, the data support the existence of positive correlation between the duration of the disease and ReHo bilaterally in precuneus/PCC and SMA. These results indicate that the involvement of these regions in specific cortical and subcortical networks may be related to the pathomechanisms of seizure generation and the neurological deficits observed in CAE patients. Previous studies have found thalamus abnormalities in CAE patients including bilateral thalamic atrophy [22–24] and neuronal metabolic dysfunction [5]. Our previous work has found that untreated CAE patients had a significantly higher fractional anisotropy value in the bilateral thalamus [4]. Most studies investigated the generalized spike-wave discharges (GSWDs) related to blood oxygenation level dependent (BOLD) change and found bilateral BOLD signal increase in thalamus during GSWDs [25,26]. Recently, decreased connectivity in the thalamus and basal ganglia and increased connectivity in the medial occipital cortex were also found [8]. In this study, we found reduced ReHo in thalamus during interictal resting state and this reflects that thalamic function is also abnormal in the “baseline” state in CAE. Resting state basal ganglia network abnormality has been found in idiopathic generalized epilepsy (IGE) [27]. GSWDs were associated with symmetrical deactivation of caudate nucleus during ictal GSWDs [28]. Another study found that a BOLD signal decrease in the head of the caudate nucleus occurred before GSWDs [29]. In consistent with
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these results, we found reduced ReHo in bilateral caudate. And in line with this, the caudate-putamen showed decreased cerebral blood flow during normal whisker stimulation in an established animal model of absence epilepsy [30]. Cerebellum is involved in IGE, and a clear activation was seen in the cerebellum in response to epileptic discharges [31,32]. Our previous study showed increased mean diffusivity values in bilateral cerebellar hemispheres in CAE [4]. On the other hand, the involvement of the cerebellum in emotional and cognitive processing has been found in many studies [33]. Our findings, in line with the above studies, found that ReHo was reduced in bilateral posterior lobe of cerebellum. This indicates that CAE patients had abnormal spontaneous neural activity in the cerebellum, which might partially underlie the neurological deficits of CAE. The insula is a functional complex region of the cortex, involved in processing affect, sensory-motor processing, and general cognition [34]. Gray matter volume decrease [35] and increased activity bilaterally in the insula have been found in IGE [36,37]. In line with these reports, we also found the increase of ReHo in bilateral insula. ReHo increase in insula may reflect compensatory increased spontaneous activity in CAE patients. In addition, we found ReHo increase in left occipital cortex. In another report, it has been found occipital activation preceded the onset of GSWDs [38], and functional connectivity of the occipital cortex was increased to primary visual, auditory and sensorimotor cortical regions [39]. These findings suggest that the occipital cortex may play an important role in absence epilepsy that warrants further investigation. In our study, decreased ReHo was mainly in precuneus, PCC, and inferior parietal lobule. These brain regions overlap with the components of DMN that is involved in the integration of cognitive and emotional processing and monitoring the world around us. ReHo decrease in these areas indicates DMN abnormalities in CAE and this is also supported by other reports [6]. What is more, positive correlations between the ReHo values in the precuneus/PCC and SMA and the disease duration were identified. These results suggest that these regions may have a role in the chronicity of CAE. The precuneus/PCC which has been revealed by group comparison is the hubs of DMN network. Although the correlation in SMA was not within the region in which patients showed deficits in ReHo, SMA has been considered to play an important role in linking cognition to action [40]. These results indicate that along with disease duration, ReHo increase in these areas may reflect compensatory increased spontaneous activity for the disrupted global cognitive function in patients. Some other issues should be mentioned. First, the sample size is modest; it is possible that larger sample size will provide further insights. However, it should be noted that our group was a homogeneous group of patients without medication. Second, the cardiac and respiratory signals were not recorded. Third, simultaneous EEG during the fMRI scanning was not performed here, and it is impossible to rule out the confounding epileptic discharges during MR scanning. Fourth, we cannot exclude the possibility that the group differences partly resulted from spontaneous thoughts and internal cognitive processing during the scanning.
5. Conclusions The current findings demonstrated alterations of ReHo in the striatothalamo-cortical network in drug naïve CAE subjects during interictal resting state. Some regions with decreased ReHo followed the pattern of ‘default’ state of brain function. In addition, positive correlations between the ReHo values in the precuneus/PCC and SMA and the disease duration were identified. These results indicate that the involvement of these regions may be related to the pathomechanisms of seizure generation and the neurological deficits observed in CAE patients. ReHo has demonstrated the capability to characterize spontaneous brain dysfunction in epilepsy.
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Disclosure of conflicts of interest None of the authors has any conflict of interest to disclose. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines. Acknowledgments This work was supported by the National Natural Science Foundation of China (Contract grant number: 30811120424, 81100974/H091, 30870655 and 81071222), grant from the Sichuan University Research Fund (No: 2010SCU11034), grant form China Postdoctoral Science Foundation (No: 20100481388 and No: 201104692), the 973 Project 2011CB707803, and the 111 project. The authors would like to acknowledge all participants in the study. References [1] Caplan R, Siddarth P, Stahl L, Lanphier E, Vona P, Gurbani S, et al. Childhood absence epilepsy: behavioral, cognitive, and linguistic comorbidities. Epilepsia 2008;49:1838–46. [2] Caplan R, Levitt J, Siddarth P, Wu KN, Gurbani S, Sankar R, et al. Frontal and temporal volumes in childhood absence epilepsy. Epilepsia 2009;50:2466–72. [3] Cohen AS, Daley M, Siddarth P, Levitt J, Loesch IK, Altshuler L, et al. Amygdala volumes in childhood absence epilepsy. Epilepsy Behav 2009;16:436–41. [4] Yang T, Guo Z, Luo C, Li Q, Yan B, Liu L, et al. White matter impairment in the basal ganglia-thalamocortical circuit of drug-naive childhood absence epilepsy. Epilepsy Res 2012;99:267–73. [5] Fojtikova D, Brazdil M, Horky J, Mikl M, Kuba R, Krupa P, et al. Magnetic resonance spectroscopy of the thalamus in patients with typical absence epilepsy. SeizureEur J Epilep 2006;15:533–40. [6] Yang T, Luo C, Li Q, Guo Z, Liu L, Gong Q, et al. Altered resting-state connectivity during interictal generalized spike-wave discharges in drug-naive childhood absence epilepsy. Hum Brain Mapp 2013;34:1761–7. [7] Killory BD, Bai X, Negishi M, Vega C, Spann MN, Vestal M, et al. Impaired attention and network connectivity in childhood absence epilepsy. Neuroimage 2011;56:2209–17. [8] Masterton RA, Carney PW, Jackson GD. Cortical and thalamic resting-state functional connectivity is altered in childhood absence epilepsy. Epilepsy Res 2012;99:327–34. [9] Zang Y, Jiang T, Lu Y, He Y, Tian L. Regional homogeneity approach to fMRI data analysis. Neuroimage 2004;22:394–400. [10] Zhang Z, Liu Y, Jiang T, Zhou B, An N, Dai H, et al. Altered spontaneous activity in Alzheimer's disease and mild cognitive impairment revealed by Regional Homogeneity. Neuroimage 2012;59:1429–40. [11] Wu T, Long X, Zang Y, Wang L, Hallett M, Li K, et al. Regional homogeneity changes in patients with Parkinson's disease. Hum Brain Mapp 2009;30:1502–10. [12] Alexander-Bloch AF, Gogtay N, Meunier D, Birn R, Clasen L, Lalonde F, et al. Disrupted modularity and local connectivity of brain functional networks in childhood-onset schizophrenia. Front Syst Neurosci 2010;4:147. [13] Wu QZ, Li DM, Kuang WH, Zhang TJ, Lui S, Huang XQ, et al. Abnormal regional spontaneous neural activity in treatment-refractory depression revealed by resting-state fMRI. Hum Brain Mapp 2011;32:1290–9. [14] Zhu CZ, Zang YF, Liang M, Tian LX, He Y, Li XB, et al. Discriminative analysis of brain function at resting-state for attention-deficit/hyperactivity disorder. Med Image Comput Comput Assist Interv 2005;8:468–75. [15] Zhong Y, Lu G, Zhang Z, Jiao Q, Li K, Liu Y. Altered regional synchronization in epileptic patients with generalized tonic–clonic seizures. Epilepsy Res 2011;97:83–91. [16] Mankinen K, Long XY, Paakki JJ, Harila M, Rytky S, Tervonen O, et al. Alterations in regional homogeneity of baseline brain activity in pediatric temporal lobe epilepsy. Brain Res 2011;1373:221–9.
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