YEBEH-04193; No of Pages 8 Epilepsy & Behavior xxx (2015) xxx–xxx

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Altered attention networks in benign childhood epilepsy with centrotemporal spikes (BECTS): A resting-state fMRI study Fenglai Xiao a, Lei Li b, Dongmei An a, Du Lei b, Yingying Tang a, Tianhua Yang a, Jiechuan Ren a, Sihan Chen a, Xiaoqi Huang b, Qiyong Gong b,⁎⁎, Dong Zhou a,⁎ a b

Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China

a r t i c l e

i n f o

Article history: Received 2 December 2014 Revised 3 January 2015 Accepted 12 January 2015 Available online xxxx Keywords: Benign childhood epilepsy with centrotemporal spikes Attention deficit and hyperactivity disorder fMRI Functional connectivity

a b s t r a c t It is noteworthy that some children with benign childhood epilepsy with centrotemporal spikes (BECTS) show attention problems despite their favorable seizure outcome. Resting-state functional magnetic resonance imaging (fMRI) is a method widely used to detect brain network alterations in neuropsychiatric diseases. We used resting-state functional magnetic resonance imaging (fMRI) to investigate specific brain networks related to attention deficit in children with BECTS. Resting-state fMRI was performed in patients with BECTS with ADHD (n = 15) and those with BECTS without ADHD (n = 15) and in healthy controls (n = 15). Unbiased seedbased whole-brain functional connectivity analysis was used to study the connectivity pattern of three resting-state networks, including the ventral attention network (VAN) and the dorsal attention network (DAN) and the default mode network (DMN). Patients with BECTS with ADHD displayed decreased functional connectivity in the DAN compared with other two groups, while patients with BECTS without ADHD showed increased functional connectivity in the DAN. Moreover, we found increased functional connectivity in the VAN and in the DMN in patients with BECTS with or without ADHD when comparing with controls. These results showed that the newly-diagnosed children with BECTS displayed brain activity alterations in the ventral and dorsal attention networks. The difference in the extent of impairment in the dorsal attention network of patients with BECTS with ADHD and patients with BECTS without ADHD may lead to improved understanding of the underlying neuropathophysiology and treatment of BECTS with ADHD and BECTS without ADHD. © 2015 Elsevier Inc. All rights reserved.

1. Introduction Benign childhood epilepsy with centrotemporal spikes (BECTS) or rolandic epilepsy is the most common childhood epilepsy syndrome, representing 15–25% of all childhood epilepsy cases [1]. The semiology is characterized by brief, simple, partial orofacial motor or sensory seizures during sleep or upon awakening with or without secondary generalization [1], with a typical electroencephalogram (EEG) that shows centrotemporal spikes [2]. Although BECTS is considered benign with its seizure outcomes, formal neuropsychological evaluations have revealed a higher prevalence of behavioral problems such as attention deficit and hyperactivity

⁎ Correspondence to: D. Zhou, Department of Neurology, West China Hospital, Sichuan University, 610041 Chengdu, People's Republic of China. ⁎⁎ Correspondence to: Q. Gong, HMRRC in West China Hospital, Sichuan University, 610041 Chengdu, People's Republic of China. Tel./fax: +86 28 85422548. E-mail addresses: [email protected] (Q. Gong), [email protected] (D. Zhou).

disorder (ADHD) in children with BECTS than in healthy sex- and agematched children [3]. Benign childhood epilepsy with centrotemporal spikes and attention deficit and hyperactivity disorder share some clinical features [4]: both disorders start in early childhood and occur somewhat more frequently in boys than in girls, e.g., as in BECTS, some cases in ADHD seem to be limited by puberty. Neuropsychological and behavioral similarities are, for example, evidenced in deficits of executive functions, inhibition of control, and externalizing behavioral symptoms [5,6]. Electroencephalographic hints of cerebral immaturity are another similarity [7–9]. The frequency of spikes in rolandic areas in children with ADHD is significantly higher than what is expected from epidemiologic studies, and children with rolandic spikes tended to exhibit more hyperactive–impulsive symptoms [8]. On the other hand, attention impairment appears to be correlated not specifically with seizure activity but with EEG activity in BECTS [10]. A clear correlation occurs between the improvement in attention measures and the spontaneous, or drug-induced, diminution or resolution of EEG abnormalities in longitudinal studies of BECTS [11]. These observations suggest that these two features may share a correlated maturational pathology.

http://dx.doi.org/10.1016/j.yebeh.2015.01.016 1525-5050/© 2015 Elsevier Inc. All rights reserved.

Please cite this article as: Xiao F, et al, Altered attention networks in benign childhood epilepsy with centrotemporal spikes (BECTS): A restingstate fMRI study, Epilepsy Behav (2015), http://dx.doi.org/10.1016/j.yebeh.2015.01.016

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F. Xiao et al. / Epilepsy & Behavior xxx (2015) xxx–xxx

2. Methods and materials

and gender (p = 0.757) were not significantly different between the patient group and the healthy control group (age: 8.4 ± 2.1 v.s 8.7 ± 2.2 v.s 8.2 ± 1.9; gender: 9 girls v.s 9 girls v.s 10 girls) (see Table 1 for details). All subjects were administered the Wechsler Intelligence Scale for Children — China Revised (WISC-CR) test, and all had a full-scale IQ of N75. The Diagnostic and Statistical Manual of Mental Disorders — Fourth Edition (DSM-IV) criteria for ADHD were scored by a neuropsychologist and were used along with age, gender, and handedness to match cohorts of patients and controls: the diagnosis of ADHD was made if more than six out of nine symptoms of inattention or hyperactivity– impulsivity had persisted for at least six months using the questionnaire of ADHD symptoms for parents. For each item, the scores are calculated from four levels: never or rarely (0), sometimes (1), often (2), and very often (3). The sum of all the scores on the 18 items results in a total score for the total scale [22]. Fifteen patients were diagnosed with ADHD (see Table 1 for details). Different aspects of language abilities were examined in these children using the phonological awareness test, morphological awareness test [23], and expressive language test to screen out severe language impairments. The details are in Table 1. Twenty-four-hour video-EEG was performed in all patients before diagnosis; provocative tests such as photic stimulation and hyperventilation were also performed at the same time. Electroencephalogram background rhythm was normal for age in all patients. All the patients received simultaneous EEG–fMRI scanning, and none of the patients experienced seizures 15 days before the scanning. No EEG data were obtained from controls during the scans although the cap remained in place to ensure that the experience for patients and that for controls were identical. A built-in camera was used to monitor the state of subjects and to see whether the patients were asleep during scanning. The local research ethics committee approved this study, and written informed consent was obtained from parents or caregivers.

2.1. Subjects

2.2. Data acquisition

The patients were diagnosed with BECTS at the Epilepsy Clinic of the Department of Neurology, West China Hospital of Sichuan University from 1 December 2010 to 30 May 2013. Patient selection was based on criteria concerning seizure semiology and EEG as described in the literature [20]. The inclusion criteria were as follows: (1) newly diagnosed BECTS according to the current clinical and electroencephalographic criteria adopted by the ILAE [21]; (2) the presence of the specific EEG characteristics at any time before or during the study and an agetypical EEG background rhythm; (3) typical seizures that were hemifacial, characterized by hemifacial clonic manifestations that were sometimes preceded by unilateral paresthesia involving the tongue, lips, gums, and cheek, and jerks that were often associated with lateral tonic deviations of the mouth involving the lips and tongue and which resulted in drooling due to sialorrhea with or without secondary generalization; (4) no other neurologic, psychiatric, or somatic disorders or aphasia; (5) no evidence of structural brain damage based on magnetic resonance imaging; (6) normal neurological and mental status; (7) no comorbidities such as tic disorder, oppositional or conduct disorder, anxiety, depression, and learning disabilities; and (8) frequent interictal epileptic discharges during wakefulness (N10/ min). To exclude severe cases (Landau–Kleffner syndrome (LKS) or LKS-like), interictal epileptiform activity was required to be present b85% of the time during non-REM sleep. Children were excluded if they had dental braces (MRI quality), were somewhat afraid in the scanner, or fell asleep during the scanning. Ultimately, thirty patients (11 girls, 19 boys) with BECTS were included. All patients were newly diagnosed, took no medication before scanning, and started treatment with antiepileptic medication after the scanning. Fifteen healthy controls from the local community were recruited. None of the healthy controls had a history of neurological/ psychiatric disorders or attended special education. Age (p = 0.529)

Magnetic resonance images detecting BOLD signal were obtained using a Trio Tim (3 T) magnetic resonance (MR) imaging system (Siemens, Erlangen) with a gradient-echo echo-planar imaging sequence: repetition time/echo time (TR/TE), 2000/30 ms; voxel size, 3.75 × 3.75 × 5 mm3; flip angle, 90°; slice thickness, 5 mm (no gap); matrix, 64 × 64; and FOV, 240 × 240 mm2. Each brain volume comprised 30 axial slices, and each functional run contained 200 volumes, with a total scan time of 406 s. The three-dimensional T1-weighted magnetization-prepared rapidly acquired gradient-echo (MPR) images were also acquired (TR = 1900 ms; TE = 2.26 ms; FOV = 256 × 256 mm2; flip angle = 9°; matrix = 320 × 320; 176 slices per lab; slice thickness = 1 mm). During the resting-state scans, a standard birdcage head coil was used together with a restraining foam pad to minimize head motion and to diminish scanner noise. Subjects were instructed to remain still, focus their thoughts on anything, and keep their eyes closed. The EEG data were acquired using an MR-compatible EEG system (BrainProducts, Germany) with 32 scalp channels positioned according to the international 10–20 system. The signals were recorded by using the BrainProducts Recorder software at a sampling rate of 5000 Hz locked precisely to the MR system clock (10 MHz) using the BrainProducts SyncBox and filtered online via a low-pass hardware filter at 250 Hz. Reference and ground channels were located anterior and posterior to Fz, respectively. Electrocardiogram signals were also recorded using a single electrode attached to the participant's back, approximately 4 cm left of the spine. Impedance values were kept below 10 kΩ for EEG channels and 15 kΩ for ECG channels. Raw EEG data were processed offline to remove the artifact generated by MRI scans, allowing the visualization of the entire EEG trace. Magnetic resonance artifact was subtracted using adaptive noise cancelation software. After the artifact removal, EEG data were low-pass filtered with a cutoff

Through examining the human brain as an integrative network of functionally interacting brain regions, we can obtain new insights about large-scale neuronal communication in the brain [12]. Restingstate functional MRI connectivity measures the synchronization in slow blood oxygen level-dependent signal fluctuations between different brain regions at rest [13]. It has been used to examine how functional connectivity relates to human behavior and how this organization may be altered in brain diseases [14]. The ventral attention network (VAN) uses the temporoparietal junction (TPJ) and the ventral frontal cortex (VFC) to reorient attention to salient behaviorally relevant stimuli [15]. The bilateral dorsal attention network (DAN) uses regions such as the intraparietal sulcus (IPS) and the frontal eye field (FEF) to enable the control of spatial attention through the selection of sensory stimuli based on internal goals or expectations and links them to appropriate motor responses [15]. Altered spontaneous brain activity was found in these two specific networks in ADHD - dorsal attention network (DAN) and ventral attention network (VAN) - which has clinical implications in attention impairments [16–18]. In addition, the other important system, the default mode network (DMN), contains the precuneus/posterior cingulate cortex, medial prefrontal cortex, and dorsal anterior cingulate cortex and acts as a form of functional connectivity baseline thought to reflect intrinsic brain activity [19]. Therefore, we speculated that BECTS could cause dysfunction in these specific networks involved in ADHD, and in patients with BECTS with ADHD, the alteration in these networks might be more marked. In the present study, we used resting-state fMRI to investigate altered resting-state functional connectivity in untreated BECTS. We examined the three different networks mentioned above: the VAN, the DAN, and the DMN.

Please cite this article as: Xiao F, et al, Altered attention networks in benign childhood epilepsy with centrotemporal spikes (BECTS): A restingstate fMRI study, Epilepsy Behav (2015), http://dx.doi.org/10.1016/j.yebeh.2015.01.016

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Table 1 Demographic, clinical, EEG, and neuropsychological results for patients with BECTS with or without attention deficit and hyperactivity disorder and for healthy controls. Characteristics

Male: female Age, years Age at onset Mean ± SD (range) b6 years n (%) N6 years n (%) Seizure frequency Mean ± SD (range) b5 n (%) N5 n (%) EEG characteristics CT only CT–CFT CT–CPT Secondarily generalized seizures Family history Febrile convulsions EEG status during scanning No spikes Sporadic Estimated IQ Mean ± SD Verbal IQ Mean ± SD Phonological awareness test Mean ± SD Morphological awareness test Mean ± SD Boston naming test Mean ± SD Total score of DSM-IV for ADHD Mean ± SD

Group

p

HCs n = 15

Patients with BECTS n = 15

Patients with BECTS-A n = 15

9:6 8.4 ± 2.1

10:5 8.7 ± 2.2

9:6 8.2 ± 1.9

0.757 0.529

7.3 ± 2.0 (3–10) 7 (46.7%) 8 (53.3%)

7.5 ± 1.7 (4–9) 5 (33.3%) 10 (66.7%)

0.775 0.325

2.8 ± 1.9 (1–9) 11 (73.3%) 4 (26.7%)

2.6 ± 1.5 (1–8) 12 (80.0%) 3 (20.0%)

0.84 0.50

9 (60%) 4 (26.7%) 2 (13.3%) 5 (33.3%) 4 (26.7%) 2 (13.4%)

6 (40%) 4 (26.7%) 5 (33.3%) 6 (40%) 2 (13.4%) 1 (6.7%)

7 (46.7%) 8 (53.3%)

10 (66.7%) 5 (33.3%)

102.4 (5.5)

101.2 (4.6)

101.9 (5.8)

96.3 (7.0)

97.1 (6.8)

95.9 (6.3)

12.6 (4.5)

11.4 (4.0)

12.1 (4.3)

16.4 (4.3)

15.2 (3.8)

15.7 (4.6)

39.6 (7.1)

36.7 (6.6)

37.8 (5.5)

2.7 (1.6)

3.9 (1.0)

9.5 (2.0)

0.19

0.71 0.326 0.50 0.231

0.105 0.88 0.548 0.679 0.463 b0.001

Abbreviation: BECTS: benign childhood epilepsy with centrotemporal spikes without attention deficit and hyperactivity disorder; BECTS-A: benign childhood epilepsy with centrotemporal spikes with attention deficit and hyperactivity disorder; CT: centrotemporal; CPT: centroparietotemporal; CFT: centrofrontotemporal; IQ: intelligence quotient; L: left; R: right; HCs: healthy controls; SD: standard deviation.

frequency of 25 Hz and were visually inspected [24]. A trained epilepsy neurologist reviewed the filtered data to identify the epileptic spikes that occurred during scans. 2.3. Data preprocessing The preprocessing was performed by using the Data Processing Assistant for Resting-State fMRI (DPARSF) with statistical parametric mapping (SPM8, http://www.fil.ion.ucl.ac.uk/spm) [25]. We selected the fMRI data of the single run in the session where simultaneous EEG recording showed no spikes or displayed sporadic spikes to minimize the effects of epileptic discharges. Conversion of the DICOM data to NIFTI images, removal of the first 5 time points from each patient's data, slice-timing correction, realignment to the middle image, spatial normalization to the Montreal Neurological Institute (MNI) template, resampling of each voxel to 3 × 3 × 3 mm3, and spatial smoothing with a 6-mm full-width at half-maximum (FWHM) Gaussian kernel were included. Data were excluded if motion parameters exceeded 3 mm in any direction or 3.0° of any angular motion during the scan. Several procedures were used to remove the possible variances from time course of each voxel: (i) temporal band-pass filtering (pass band: 0.01–0.1 Hz) was conducted through a phase-insensitive filtering and (ii) through linear regression, the time series was further corrected to eliminate the effect of six head motion parameters obtained from the realigning step and the effect of the signals from a CSF region and a white matter (WM) region. The residuals of the regressions were linearly detrended and were then used for the RSFC analysis. The RSFC was processed with software REST [26]. On the basis of the previous studies [18,27], the following seed regions of interest with a 6-mm radius were selected as seeds to identify three different

networks — the precuneus (± 7, − 60, 21) [27] in the DMN; the temporoparietal junction (TPJ) (± 53, − 48, 20) [18] and the ventral frontal cortex (VFC) (± 37, − 18, 1) [18] in the VAN; and the intraparietal sulcus (IPS) (± 27, − 58, 49) [18] and frontal eye field (FEF) (±24, −13, 51) [18] in the DAN. For each region, the time course from the bilateral spherical region of interest was averaged. Correlation functional analyses were performed by computing the temporal correlation between each seed reference and the rest of the brain in a voxel-wise manner. Individual correlation coefficients were normalized to Z-scores by using Fisher's Z-transformation. Finally, the individual Z-scores were entered into a random effect one-sample t-test in SPM8. Age and sex were used as confounding variables in all statistical analyses. A statistical map of significant functional connectivity was created for each seed. The significance level was set at p b 0.05. 2.4. Statistical analysis Differences between groups in terms of demographic and clinical variables were performed by the Pearson χ2 test, one-way analysis of variance (ANOVA), or the Student t-test, as appropriate. The functional networks of attention are presumably affected in both groups of patients. We analyzed REST [26] based on SPM8 using one-way ANOVA to determine significant differences in functional connectivity across 3 groups. The threshold applied by the whole-brain functional connectivity analysis might be too stringent to detect some subtle functional changes. The post hoc two-sample t-tests for each paired group were used to explore whether some revealed changes in functional connectivity of attention networks are evident in both patient groups using software. The statistical significance level was set at p b 0.05. The specific masks were created by combining the voxels for

Please cite this article as: Xiao F, et al, Altered attention networks in benign childhood epilepsy with centrotemporal spikes (BECTS): A restingstate fMRI study, Epilepsy Behav (2015), http://dx.doi.org/10.1016/j.yebeh.2015.01.016

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F. Xiao et al. / Epilepsy & Behavior xxx (2015) xxx–xxx

each pairing (BECTS with ADHD and controls, BECTS without ADHD and controls, and BECTS without ADHD and BECTS with ADHD) that were obtained from one-sample t-test results. The mean time course of each ROI was correlated with every other ROI to obtain a 10 × 10 matrix of correlation coefficients (r) in every participant. The group difference comparisons were restricted to corresponding positive functional connectivity voxels. The correction threshold was determined by a Monte Carlo simulation [25] (see AlphaSim in AFNI http://afni.nimh.nih.gov/ pub/dist/doc/manual/AlphaSim.pdf). Given that RSFC could be affected by micromotions from volume to volume, the framewise displacement (FD) values for each subject were computed, and then the mean FD was applied as a covariate in the group comparisons of RSFC. We used commercially available software (SPSS, version 19; SPSS Inc.) to calculate Spearman's rank correlation coefficients between the individual linear correlation coefficients of each ROI (Z-score) and scores from the DSV-IV total score for ADHD and estimated IQ score using a Bonferroni-corrected p threshold of 0.05 based on regions within the each network that differed significantly between the patient and control groups [28].

3. Results The clinical characteristics of patients are in Table 1. Fifteen children with BECTS were diagnosed with ADHD. Sporadic interictal spikes (b10 during one run) were found in 13 of 30 patients during the scanning. No significant difference (F = 0.16, p = 0.85) was found in FD values among three groups (mean ± standard deviation [SD]: 0.24 ± 0.14 mm, 0.23 ± 0.12 mm, 0.21 ± 0.12 mm). Table 3 presents the between-groups differences of functional connectivity analysis, and MNI peak coordinate regions are reported.

Table 3 Brain alteration of brain network in patients with BECTS with or without ADHD and controls. Seeds and networks

Connected location

Controls vs. patients with BECTS Ventral attention network Left VFC Right fusiform gyrus Media frontal gyrus Dorsal attention network Left IPS Right middle frontal lobe Precuneus Default mode network Right precuneus Left lingual gyrus Right lingual gyrus

↑ or ↓ Size MNI coordinates X

Y

p

Z

↓ ↓

128 193

30 −51 9 54

12 ⁎ 12 ⁎

↓ ↓

338 116

39 30 3 −51

24 ⁎ 45 ⁎

↓ ↓

161 210

−7 −70 7 −70

3 ⁎ 3 ⁎

↓

162

−18 51

Controls vs. patients with BECTS-A Ventral attention network Left VFC Media frontal gyrus Dorsal attention network Left IPS Left inferior parietal lobe Default mode network Right precuneus Left lingual gyrus Right lingual gyrus

↑

122

−27 −66

42 ⁎

↓ ↓

139 136

−15 −63 15 −63

−2 ⁎ −2 ⁎

Patients with BECTS-A vs. patients with BECTS Dorsal attention network Left IPS Right middle frontal gyrus Right inferior parietal lobe

↓ ↓

164 113

34 26 39 −49

37 ⁎ 43 ⁎

−15 ⁎

Abbreviation: BECTS: benign childhood epilepsy with centrotemporal spikes without attention deficit and hyperactivity disorder; BECTS-A: benign childhood epilepsy with centrotemporal spikes with attention deficit and hyperactivity disorder; IPS: intraparietal sulcus; VFC: ventral frontal cortex. ↑: increased functional connectivity; ↓: decreased functional connectivity. ⁎ p b 0.05 AlphaSim corrected.

3.1. Functional connectivity of the VAN

3.2. Functional connectivity of the DAN

A connectivity map for each group was generated, and the connectivity patterns of positive and negative correlations appeared to be similar during visual inspection of the two groups. The VAN was observed in brain regions previously defined as within the VAN for both groups [18]. Only clusters with size greater than 50 were reported. No difference was detected between patients with BECTS with ADHD and patients with BECTS without ADHD. Comparing patients with BECTS without ADHD with controls, we detected increased connectivity between the following regions: left VFC and right fusiform gyrus, left VFC and medial frontal gyrus, and left VFC and anterior cingulate gyrus. In addition, increased connectivity between the left VFC and the medial frontal gyrus was found in patients with BECTS with ADHD relative to controls (Fig. 1, Table 2).

Dorsal attention network and between-groups differences were found. Compared with controls, patients with BECTS with ADHD or patients with BECTS without ADHD showed increased connectivity between the left IPS and right middle frontal lobe, right inferior parietal lobe, and precuneus. However, decreased connectivity was detected in patients with BECTS with ADHD. The patients with BECTS with ADHD showed less connectivity between the left IPS of the DAN and the left inferior parietal lobe relative to controls. When compared with children with BECTS without ADHD, the results revealed that decreased connectivity was detected between the left IPS of the DAN and right inferior parietal lobe, right middle frontal lobe and right superior frontal gyrus and Brodmann's area 9 (Fig. 2, Table 2).

3.3. Functional connectivity of the DMN Table 2 MNI coordinates of 10 nodes (ROI) for three networks. Network and Brain regions

Abbreviations

MNI coordinates

Default mode network Left precuneus Right precuneus Ventral attention network Left temporoparietal junction Right temporoparietal junction Left ventral frontal cortex Right ventral frontal cortex Dorsal attention network Left intraparietal sulcus Right intraparietal sulcus Left frontal eye field Right frontal eye field

DMN PRE. L PRE. R VAN TPJ. L TPJ. R VFC. L VFC. R DAN IPS. L IPS. R FEF. L FEF. R

x

MNI: Montreal Neurological Institute; ROI: region of interest.

y −60 −60

z 21 21

−53 53 −37 37

48 48 18 18

20 20 1 1

−27 27 −24 24

58 58 −13 −13

49 49 51 51

−7 7

Compared with the controls, no decrease in connectivity was found. However, increased connectivity was detected in the lingual gyrus, left calcarine, left cuneus, and Brodmann's area 19 in patients with BECTS without ADHD in comparison with controls. Results also showed similar increased connectivity in patients with BECTS with ADHD. No difference was detected between patients with BECTS with ADHD and patients with BECTS without ADHD (Fig. 3, Table 2).

3.4. Correlation results No significant correlations were found between DSM-IV for ADHD total scores, IQ scores, and language test with altered functional connectivity in the three networks when considering multiple correlations for the group with ADHD.

Please cite this article as: Xiao F, et al, Altered attention networks in benign childhood epilepsy with centrotemporal spikes (BECTS): A restingstate fMRI study, Epilepsy Behav (2015), http://dx.doi.org/10.1016/j.yebeh.2015.01.016

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Fig. 1. Comparison of connectivity maps in the ventral attention network. Regions of increased (warm colors) and decreased (cold colors) resting-state functional connectivity (FC) with seeds in the networks. Group differences were based on clusters defined by Z N 2.05 and a corrected cluster threshold of p = 0.05 (the correction threshold was determined by a Monte Carlo simulation). The final Z-statistic maps are visualized as hemispheric surfaces (A) and symmetric axial slices (B). The Z-score bar is shown at the middle of the figure. Abbreviation: L: left; R: right; BECTS: benign childhood epilepsy with centrotemporal spikes without attention deficit and hyperactivity disorder; BECTS-A: benign childhood epilepsy with centrotemporal spikes with attention deficit and hyperactivity disorder; HCs: healthy controls. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

4. Discussion In this study, we attempted to segregate the children with BECTS according to the presence of attention deficits and directly investigated the resting-state functional connectivity (RSFC) linked to attention problems in children with BECTS. Three networks were examined, namely, ventral attention network, dorsal attention network, and default mode network — VAN, DAN, and DMN, respectively. Although children with BECTS displayed increased connectivity in the VAN and in the DMN, those with BECTS with ADHD demonstrated mainly reduced connectivity in the DAN when compared with the other two groups. Here, we provide the preliminary evidence that the ADHDrelated abnormal resting-state functional architecture is present in patients with BECTS. We found decreased functional connectivity between the left intraparietal sulcus (IPS) of the DAN and the parietal lobes in patients with BECTS with ADHD. In resting-state fMRI studies of children with ADHD, loss of functional connectivity in the DAN was associated with

failure to ignore extraneous stimuli, which is one of the core symptoms of ADHD [29]. One meta-analysis suggested that the intraparietal regions of the DAN were routinely hypoactivated in cross-sectional and longitudinal functional MR imaging research on children with ADHD [30]. Moreover, it has been suggested that the decreased functional connectivity in parietal regions be linked to impaired capacity for top-down task-directed control over sensory encoding functions [31]. The inferior parietal lobe is adjacent to the IPS and is one of the important sites of functional interaction between the ventral attention system and the dorsal attention system [15]. Ventral and dorsal attention networks are both necessary and interact when attention is reoriented by behaviorally relevant environment stimuli [32]. Hence, the decreased connectivity between the IPSs with the parietal areas may be associated with the weaker link between the ventral attention system and the dorsal attention system, which may lead to the impaired attention reorientation. Our results, which demonstrate the loss of connectivity between the DAN and the parietal lobes for patients with BECTS with ADHD, may be related to the impaired attention reorientation.

Please cite this article as: Xiao F, et al, Altered attention networks in benign childhood epilepsy with centrotemporal spikes (BECTS): A restingstate fMRI study, Epilepsy Behav (2015), http://dx.doi.org/10.1016/j.yebeh.2015.01.016

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F. Xiao et al. / Epilepsy & Behavior xxx (2015) xxx–xxx

Fig. 2. Comparison of connectivity maps in the dorsal attention network. Group differences were based on clusters defined by Z N 2.05 and a corrected cluster threshold of p = 0.05 (the correction threshold was determined by a Monte Carlo simulation). The final Z-statistic maps are visualized as hemispheric surfaces (Fig. 1A) and symmetric axial slices (Fig. 1B). The Z-score bar is shown at the middle of the figure. Abbreviation: L: left; R: right; BECTS: benign childhood epilepsy with centrotemporal spikes without attention deficit and hyperactivity disorder; BECTS-A: benign childhood epilepsy with centrotemporal spikes with attention deficit and hyperactivity disorder; HCs: healthy controls.

Considering the relatively short duration of our patients' epilepsy, we conclude that this alteration may not be due to the recurrent seizures and chronic antiepileptic drug use. Our results therefore suggest a common etiology between these two conditions (BECTS and ADHD). We did not detect the difference in the VAN between patients with BECTS with ADHD and patients with BECTS without ADHD. Nevertheless, when comparing patients with BECTS with controls, we observed increased connectivity between the ventral frontal cortices of the VAN that may reflect deficient selective attention control, specifically the intensified distractibility [33]. Although hypoconnectivity of the VAN underpins difficulties in detecting regularities or irregularities in the environment [34], hyperconnectivity was also observed in previous functional MR imaging studies of children with ADHD [30]. Moreover, the increased connectivity in the ventral frontal cortex is associated with the genetic vulnerability to ADHD [35]. Since the suppression of this network is necessary to prevent shifts of attention to irrelevant objects [36], hyperconnectivity of regions in the VAN might underpin distractibility. Increased distractibility may produce a cascade of secondary impairments in behavior, self-regulation of affect, and

motivation in children with BECTS [4]. Moreover, we found increased connectivity in the medial frontal gyrus (Brodmann's area 8, B8). The B8 area is involved in the management of uncertainty [37], which is critical in impulsivity. Hence, our results may shed light on the previous studies of the neuropsychological profile of children with BECTS who displayed increased distractibility and impulsivity [38,39]. The RSFC within the DMN was not significantly changed between children with BECTS with ADHD and children with BECTS without ADHD with regard to a p b 0.05 whole-brain correction. However, we found a trend for heightened connectivity with the DMN between visual areas in children with BECTS compared with controls. Our finding of increased connectivity between the DMN and the visual regions in patients with BECTS has not previously been reported, although one task-related fMRI publication noted decreased connectivity in the posterior cingulate cortex in patients with BECTS [40]. For patients with ADHD, substantial hyperactivation, particularly in the DMN and in the visual regions, supports ADHD as a disorder characterized not only by functional deficiencies but also by possible compensatory mechanisms [30]. In addition, this finding is supported by a study showing

Please cite this article as: Xiao F, et al, Altered attention networks in benign childhood epilepsy with centrotemporal spikes (BECTS): A restingstate fMRI study, Epilepsy Behav (2015), http://dx.doi.org/10.1016/j.yebeh.2015.01.016

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Fig. 3. Comparison of connectivity maps in the default mode network. Group differences were based on clusters defined by Z N 2.05 and a corrected cluster threshold of p = 0.05 (the correction threshold was determined by a Monte Carlo simulation). The final Z-statistic maps are visualized as hemispheric surfaces (Fig. 1A) and symmetric axial slices (Fig. 1B). The Z-score bar is shown at the middle of the figure. Abbreviation: L: left; R: right; BECTS: benign childhood epilepsy with centrotemporal spikes without attention deficit and hyperactivity disorder; BECTS-A: benign childhood epilepsy with centrotemporal spikes with attention deficit and hyperactivity disorder; HCs: healthy controls.

that children with BECTS had greater susceptibility to distracters in their visual field compared with healthy children or children affected by idiopathic generalized epilepsies [41]. On the other hand, one recent fMRI study noted activation of the lingual gyrus in disorders of consciousness using resting-state fMRI, suggesting hyperactive awareness to external surroundings [42]. Our result, namely increased connectivity between the DMN and the lingual gyrus, may reflect the heightened sensitivity of patients with BECTS to their surrounding environment [42]. Several issues must be addressed further. First, the group size was relatively small, and the findings of our study will require replication and confirmation in a larger sample size. Second, resting-state fMRI has been widely applied to clinical studies because of its simplicity. The relation between brain function change in resting-state and traditional task fMRI has been discussed [43]. The task activation could be partly explained by resting-state brain activity, but in pathological conditions, resting-state rather than task fMRI could be more sensitive to the functional alteration [44]. Thus, these two modalities are closely related, although they sometimes lead to different results. Third, we

used simultaneous EEG–fMRI to monitor the interictal discharges during scanning and to minimize the confounding factors of interictal discharges by ruling out the sessions in which patients exhibited frequent spikes. Still, there were several patients who showed sporadic spikes during the scanning. It has been suggested that frequent interictal discharges might be associated with impairment of selective visual attention [45]. The vulnerability to interictal discharges is associated with worse neurocognitive outcomes [46]. Low-dosage valproic acid and levetiracetam were prescribed for initial treatment (started at 5 mg/kg/day) after the scanning. Our ongoing study will explore the recovery of attention disturbances with antiepileptic medication treatment. Our further longitudinal EEG–fMRI studies could partly help explore the relationship between interictal discharges and neurocognitive and attention outcomes. Repeat EEG will be done to see if there is a positive electrophysiological recovery and whether there is a positive correlation between the imaging alterations and attention deficit recovery. Finally, more comprehensive neuropsychological tests are needed to screen affective processing and specific language function.

Please cite this article as: Xiao F, et al, Altered attention networks in benign childhood epilepsy with centrotemporal spikes (BECTS): A restingstate fMRI study, Epilepsy Behav (2015), http://dx.doi.org/10.1016/j.yebeh.2015.01.016

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Please cite this article as: Xiao F, et al, Altered attention networks in benign childhood epilepsy with centrotemporal spikes (BECTS): A restingstate fMRI study, Epilepsy Behav (2015), http://dx.doi.org/10.1016/j.yebeh.2015.01.016