Development of Brain Network in Children with Autism from Early Childhood to Late Childhood

Accepted Manuscript Research Paper Development of Brain Network in Children with Autism from Early Childhood to Late Childhood Junxia Han, Ke Zeng, Jiannan Kang, Zhen Tong, Erjuan Cai, He Chen, Meng Ding, Yue Gu, Gaoxiang Ouyang, Xiaoli Li PII: DOI: Reference:

S0306-4522(17)30743-1 https://doi.org/10.1016/j.neuroscience.2017.10.015 NSC 18080

To appear in:

Neuroscience

Received Date: Accepted Date:

2 August 2017 12 October 2017

Please cite this article as: J. Han, K. Zeng, J. Kang, Z. Tong, E. Cai, H. Chen, M. Ding, Y. Gu, G. Ouyang, X. Li, Development of Brain Network in Children with Autism from Early Childhood to Late Childhood, Neuroscience (2017), doi: https://doi.org/10.1016/j.neuroscience.2017.10.015

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Development of Brain Network in Children with Autism from Early Childhood to Late Childhood

Junxia Han1, Ke Zeng1, Jiannan Kang2, Zhen Tong2, Erjuan Cai2, He Chen1, Meng Ding3, Yue Gu4, Gaoxiang Ouyang1, Xiaoli Li1* 1

State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China;

2

Institute of Electrical Engineering, Yanshan University, Qinhuangdao,066004,China;

3

College of Electronic & Information Engineering, Hebei University, Baoding, China;

4

College of Computer Science, Tianjin University of Technology, Tianjin 300384, China

*Correspondence: [email protected]; Tel.: +86 01058802032 (Xiaoli Li)

Abstract Extensive studies have indicated brain function connectivity abnormalities in autism spectrum disorder (ASD). However, there is a lack of longitudinal or cross-sectional research focused on tracking age-related developmental trends of autistic children at an early stage of brain development or based on a relatively large sample. The present study examined brain network changes in a total of 186 children both with and without ASD from 3 to 11 years, an early and key development period when significant changes are expected. The study aimed to investigate possible abnormal connectivity patterns and topological properties of children with ASD from early childhood to late childhood by using resting-state electroencephalographic (EEG) data. The main findings of the study were as follows: (1) From the connectivity analysis, several inter-regional synchronizations with reduction were identified in the younger and older ASD groups, and several intra-regional synchronization increases were observed in the older ASD group. (2) From the graph analysis, a reduced clustering coefficient and enhanced mean shortest path length in specific frequencies was observed in children with ASD. (3) Results suggested an age-related decrease of the mean shortest path length in the delta and theta bands in TD children, whereas atypical age-related alteration was observed in the ASD group. In addition, graph measures were correlated with ASD symptom severity in the alpha band. These results demonstrate that abnormal neural communication is already present at the early stages of brain development in autistic children and this may be involved in the behavioral deficits associated with ASD.

Keywords: Autism spectrum disorder, Brain Development, Graph analysis, Connectivity, EEG, Resting state

Introduction Human brain development, especially during the first decades of the life-span, is a critical aspect of development, associated with physical, cognitive, and social-emotional development (Vertes & Bullmore, 2015). A complex network of connections is formed and refined in the brain during early and middle childhood through synaptogenesis, pruning and myelination (Chechik, Meilijson, & Ruppin, 1998). Childhood is also the window periods for the highest incidence of many neurological disorders (Ryland, Hysing, Posserud, Gillberg, & Lundervold, 2012), including the autism spectrum disorders (ASD). ASD are neurodevelopmental disorders clinically defined by some core characteristics including impaired social interaction and communication, restricted interest and behaviors and repetitive stereotypical behaviors (Association, 2013).The most obvious signs and symptom of autism start to appear between the ages of two and three. The prevalence of ASD was approximately 2.24% (1 in 45) in United States in 2014 according to Centers for Disease Control and Prevention’s (CDC) National Center for Health Statistics report (Zablotsky, Black, Maenner, Schieve, & Blumberg, 2015). Most individuals with ASD show symptoms of abnormal sensory and perceptual processing (Dawson et al., 2002; Minshew, Sweeney, & Luna, 2002).However, it is

not yet clear how the high rate of emerging psychiatric disorders in

childhood and adolescence are linked to the developmental brain processes going on at this time.

Numerous studies have demonstrated that oscillatory activity differences exist between individuals with ASD and typically developing controls (Bangel et al., 2014; Wang et al., 2013). EEG has been proven a promising tool for evaluating brain activity due to its noninvasive method of measuring neurophysiological oscillations and dynamics on a millisecond scale (Jeste, Frohlich, & Loo, 2015). Some researchers (Jamal et al., 2014; Matlis, Boric, Chu, & Kramer, 2015; Murias, Webb, Greenson, & Dawson, 2007) have attempted to explore identifying biomarkers in order to understand the neural underpinnings of ASD, such as power, coherence and functional connectivity. A series of studies about connectivity have suggested that decreased long-range connections and an excess of short-range connections are evident in children with ASD (Ghanbari et al., 2015). Reduced intra-hemispheric coherence in the delta and theta bands involving medium to long inter-sensor distances has been reported in children with ASD (Coben, Clarke, Hudspeth, & Barry, 2008). In fMRI studies (McAlonan et al., 2009; Ecker et al., 2012), the results reveal that the volume of white matter of individuals with ASD presents with widespread reduction at different life stages when compared to age-matched typically developing controls(Anagnostou & Taylor, 2011). Just et al. proposed that autistic behavior is negatively correlated with

frontal-posterior functional connectivity (Just, Keller, Malave, Kana, & Varma, 2012). Compared to the commonly used approaches such as coherence or the imaginary component of coherence, phase lag index has been reported to be far less affected by the influence of volume conduction and actively referenced electrode (Stam, Nolte, & Daffertshofer, 2007). Considering that ASD is increasingly considered to be a disease of the large-scale brain networks (Menon, 2011), there are certain limitations to using conventional analyses, such as the power of EEG oscillations which only depict abnormalities in the local brain region. It is unclear how these local abnormalities influence the functioning of the brain as an integrated system. Thus, the brain network analysis method based on graph theory estimating whole brain activities in multiple brain regions is considered to be more suitable. The network can reflect interactions between different brain regions rather than merely local dysfunctions. In this study, we compared the connectivity differences involving different brain regions and using phase lag index (PLI), and then we examined the graph-theoretical measures to quantify the properties of the connectivity matrices.

Describing brain network properties by employing graph theoretical parameters has been proven available to provide biomarkers for identifying disease (Tijms et al., 2013; Zeng et al., 2015). Graph theory can be applied to depict the connectivity data for every sub-band. Graph theory is the mathematical modeling of the organization of the whole-brain’s functional connectivity network (Stam, 2014). Graph theoretical characteristics describe the general organization and the communication efficiency of information transfer across the network and provide a theoretical basis for exploring optimal performance in the optimal network organization (Bullmore & Sporns, 2009; Sporns, 2014). A “small-world” network was proposed as being a highly efficient brain networks that is characterized by high clustering and a short path length (Bassett & Bullmore, 2006). A series of studies have reported that cognitive capability is positively correlated with higher clustering and shorter paths and disrupted brain topologies may give rise to neurological disease (Zeng et al., 2015).A few EEG and MEG studies have been employed with resting state networks in ASD children. In a previous EEG study by Barttfeld et al., the researchers showed that adults with ASD present with reduced clustering and increasing shortest path length, meaning they have inefficient local and global topology relative to typically developing controls (Barttfeld et al., 2011). Manfred G et al. reported that network abnormalities in ASD are closely associated with the sub-networks of the frontal lobe and that these measures correlate with ASD severity in the beta and gamma bands (Kitzbichler et al., 2015). Bathelt et al. reported a pattern of closer functional integration with increasing age by applying graph theory to analyze the brain network organization of typically developing children from 2 to 6 years old (Bathelt, O'Reilly, Clayden,

Cross, & de Haan, 2013). Previous studies exploring brain network properties associated with autism include MEG recordings of individuals with ages ranging from 6-21 years (15 ASD:12.5 ± 4.45 years old and 15 control subjects:13.0 ± 4.80 years old) (Kitzbichler et al., 2015), EEG recordings of individuals with ages ranging from 6-15.5 years old (26 ASD:10.1 ± 2.3 years and 22 control subjects: 10.9 ± 2.5 years) (Ghanbari et al., 2015), and of younger children individuals (12 ASD : 3.53 ± 1.19 years and 19 control subjects: 3.35 ± 0.80 years) (Boersma et al., 2013).All the results from the studies mentioned contrasted the MEG or EEG indices; however there was a lack of longitudinal or cross-sectional research focusing on tracking age-related developmental trends and using a relatively larger sample. In contrast, the present study examined brain network changes in children with ASD aged 3-11 years, covering an early and key development period when significant changes are expected. The main goal of this study was to investigate the development of network properties and to test whether a distinct difference could be detected in every sub-band and in the topological patterns of children and age-matched typically developing children with ASD in from early to late childhood. We first aimed to test whether children with ASD have a specific loss of connectivity between the different regions and whether the topological properties would show reduced efficiency relative to the healthy controls. In addition, we explored whether the network changes in individuals with ASD would be attenuated, while the network in typically developing children would evolve with age. Lastly, we examined whether abnormalities in the network properties were correlated with symptom severity in children with ASD. To this end, we collected and analyzed the resting EEG data of children with ASD and typically developing children from the ages of 3 to 11 years, which is a critical period in brain network development (Dean et al., 2014).

Materials and Methods Participants For this study, a total of 186 children were recruited, this number consisted of 80 children with ASD (age range 3-11 years, mean age: 5.69 years, SD: 2.13 years), and 106 typical controls (age range: 3-11 years, mean age: 5.85 years, SD: 1.97 years). All children in the ASD group were recruited because they had a prior diagnosis of ASD, according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition DSM-V (Association, 2013). Individuals with ASD who had medical conditions frequently associated with ASD (e.g., abnormal discharge, fragile X syndrome) were excluded from the study. All typical controls were recruited from local kindergartens or primary schools and they were confirmed to be without any clinical psychiatric or

neurological history (e.g. developmental delay, communication disorder, ADHD).

Both groups were assessed by employing the parent report on the Autism Behavior Checklist(ABC) (Volkmar et al., 1988), Social Communication Questionnaire (SCQ) (Eaves, Wingert, Ho, & Mickelson, 2006; M. Rutter, Bailey, & Lord, 2003; M. Rutter, Bailey, Lord, Cianchetti, & Fancello, 2007) , Social Responsiveness Scale (SRS) (Constantino & Gruber, 2005), and Clancy Behavior Scale (Clancy, Dugdale, & Rendle-Short, 1969; X. Sun et al., 2013). Details of the sample demographics and behavior scores are shown in Table 1.There were no significant differences between the samples of ASD children and typical controls in either age or sex. All children in this study were right-handed. Informed consent was obtained from all children with the permission of their parents. The study was approved by the School of Psychology Research Ethics Committee of Beijing Normal University.

Table 1 Population demographics and diagnostic scores ASD children (n = 80)

TD children (n = 106)

Group difference

Mean ± SD (range)

Mean ± SD (range)

P value

Age(years)

5.69 ± 2.13 (3-11)

5.85 ± 1.97 (3-11)

p = 0.600

Male/Female

65/15

79/27

ABC score

45.36 ± 22.46

8.92 ± 16.69

CABS score

12.32 ± 5.69

NA

SRS score

89.02 ± 37.17

42.98 ± 14.69

P < 0.001

SCQ score

18.25 ± 6.74

5.76 ± 3.22

P < 0.001

P < 0.001

SCQ: Social Communication Questionnaire; SRS: Social Responsiveness Scale; ABC: Autism Behavior Checklist; CABS: Clancy Behavior Scale.

EEG Collection and Preprocessing Continuous resting state EEG signals were recorded with a 128-channel EEG system (Electrical Geodesics Inc., Eugene, OR). Before the EEG recording, amplifier noise and channel gains were measured to ensure the provision of an accurate scaling factor for the EEG signal. Scalp impedances were checked online by employing Net Station (EGI, Inc.) and were kept below 50 KΩ. Data were referenced online to Cz. EEG signals were digitized at 1,000Hz. At least five minutes of open-eye resting EEG was recorded. During the EEG recording, children were required to be seated comfortably on an armchair usually accompanied by their caregivers in a quiet room.

Offline preprocessing analysis of the resting EEG data was done by employing EEGLAB

(Delorme & Makeig, 2004) and Matlab (The Mathworks, Inc., Natick, MA). In this study, according to the standard international 10-10 electrode placement, we selected 61 interested sensors from the 128-channel GSN to ensure maximal spatial coverage of the frontal, central, temporal and occipital regions. The interested sensors were marked in different colors and differently shaped according to the brain regions as shown in Figure 1. The EEG signal was then down-sampled to 250 Hz. A notch filter centered at 50 Hz was employed to remove the line noise, and the data were then band-pass filtered (0.5-45Hz). To ensure the quality of the resting-state data quality, about 2 minutes form the middle part of the EEG data with less noise and relative quiet were selected for further analysis. The EEG data were then cut into non-overlapping segments of 4s. An artifact detection algorithm was chosen to select the segments without artifact involvement, including eye movements, eye-blinks, power supply (50 Hz), breathing, muscle movements, abrupt slopes, and outlier values (Durka, Klekowicz, Blinowska, Szelenberger, & Niemcewicz, 2003). The segment was marked as an artifact and then rejected if any of the parameters calculated for each type of artifact exceeded a particular threshold. After that, visual inspection was performed to reject those segments containing noise. Sensors were marked as bad channels during individual recording segments or throughout the entire recording by using a 200 µV threshold and these bad channels were interpolated from neighboring channels (Frohlich, Irimia, & Jeste, 2015). In summary, 2.075 ± 2.639 (mean ± std) bad channels were identified and processed, leaving 21.333 ± 4.136 segments for further analysis (ASD: 21.067 ± 4.861 versus TD: 22.023 ± 4.072).

Figure 1. Electrode map. Ten Regions of interest were selected to provide maximal spatial coverage of the different brain regions. LF (Left frontal region) = 19, 22, 23, 26; RF (Right frontal region)=2, 3, 4, 9, 124; LC (Left central region) = 13, 30, 36; RC (Right central region) = 104, 105, 112; LT (Left temporal region)=24, 27, 28, 29, 33, 34, 41, 42, 45, 46, 47, 51, 58; RT (Right temporal region)=93, 96, 97, 98, 102, 103, 108, 111, 117, 116, 122, 123; LP (left parietal region)=37, 42, 52, 60; RP (Right parietal region)=85, 87, 92, 93; LO (Left occipital region)=65, 66, 67, 70; RO (Right occipital region)=77, 83, 84, 90.

Phase lag index (PLI) based functional connectivity Resting state EEG data was analyzed at five frequency bands: the delta (1-4 Hz), theta (4-6 Hz), alpha (6-13 Hz), beta (13-30 Hz) and gamma (30-45 Hz) bands. Individual peak alpha frequency usually develops from 8.1 Hz in young children to 9.9 Hz in the elderly (Boersma et al., 2011; Cragg et al., 2011). Because there are differences between individuals in their peak alpha frequency , the EEG signals were filtered into the alpha (6.0 to 13.0 Hz) such that the alpha rhythm of all subjects was taken as being from 2.0 Hz below the lowest peak frequency to 3.0 Hz above the highest peak frequency (Boersma et al., 2013).

Connectivity quantifies the relationship between EEG oscillatory activities at two nodes. The phase lag index (PLI) was chosen for this study as a measure of synchronization between oscillatory EEG activities at the two nodes. In brief, the PLI is an index describing the distribution’s asymmetry in phase differences between two time-series. The PLI is immune to the presence of common sources/volume conduction and reference electrodes.

Let ∆ϕ (tk )( k = 1,2,
, N ) be the phase differences between two time series. Then PLI can be calculated by:

PLI = sign [ ∆ϕ (tk )] where sign denotes the signum function and

⋅ denotes the mean operator. The PLI value lies

between 0 and 1. A PLI value of zero denotes either no coupling or coupling with a phase difference centered around 0 (mod π). A PLI value of 1 means perfect phase locking at a value of △φ different from 0 (mod π). The stronger this nonzero phase locking is, the larger the PLI will

be.

An undirected weighted network was employed in this study. The functional connectivity of a network of N nodes was generally denoted by a N×N symmetric matrix. The resulting PLI connectivity matrices were used to construct undirected, weighted networks in which each EEG electrode is a node, and the PLI values represent the weights of the links between nodes for each pair of sensors. In this study, PLI was calculated between all 61 electrodes for each frequency band and for each segment (each segment consisted of 1000 samples).

Graph analysis of functional brain networks

In this study, a complex network based on graph theory was constructed to describe differences in the networks of children with ASD and healthy controls. For a weighted brain network analysis, a graph of 61 nodes was constructed and the connectivity matrix calculated by PLI was the edge weights. The clustering coefficient and the shortest path length have been widely selected for the analysis of the brain function network, which quantify the extent of local interconnectivity or cliquishness in a network, and the shortest path length.

The topology features of a weighted graph can be characterized by some network indices. The clustering coefficient and average shortest path length are the two most fundamental measures, which have been widely used to analyze the brain function network. In the weighted graph, the

clustering coefficient of a node indicates the proportion of its neighbors that are connected to each other, and quantifies the tendency to form local clusters. The clustering coefficient of node i in the weighted graph can be calculated as follows: ∑ ∑,   
 = ∑ ∑,   where w is the edge weight between two nodes. The mean clustering coefficient is formally given by the following: 

1
=
 

The mean shortest path length of the network was computed by:  =

1 1 ∑ ∑ (1/ ) ( − 1)  

where lij represents the shortest path length between node i and j. Graph measures are often normalized via random surrogates. Both average absolute clustering (Cw-r) and average absolute shortest path length (Lw-r) were calculated for 100 randomized networks with similar numbers of nodes, edge weights and symmetry as the observed graph. The normalized clustering coefficient (Gamma) was defined by Cw/Cw-r, and the normalized shortest path length (Lambda) was defined by Lw/Lw-r. The small-worldness (SW) was defined as gamma/lambda (Humphries, Gurney, & Prescott, 2006).

Statistical Analysis

In this study, almost all the PLI values for the different regions at each frequency band were distributed normally after employing a Shapiro-Wilk test. Because individuals’ EEG development is known to improve with age, we separated both children with ASD and the typically developing children into two age bins as the younger and older children. For both the younger and older groups at each frequency band, four separate repeated measures ANOVAs were performed to examine for group differences using the between-subjects factor of the group (ASD vs. TD) and the region as the within-subject factors. The Greenhouse-Geisser method was adopted to adjust the degrees of freedom and to correct for lack of sphericity. For significant main effects or interactions, independent t tests were used to examine for group differences between the ASD and TD groups. For the short distance connectivity data, the repeated-measures factor had 10 levels (left and right frontal, central, parietal, temporal and occipital). For the interhemispheric connectivity data, the repeated-measures factor had 25 levels (paired-region connectivity among five regions from the

left hemispheric and right hemispheric, respectively). For the intra-hemispheric connectivity data, the repeated-measures factor had 10 levels (frontal-central, frontal-temporal, frontal-occipital, frontal-parietal, central-temporal, central-occipital, central-parietal, temporal-occipital, temporalparietal, and occipital-parietal from the same hemisphere). For the statistical graph measures analysis, we employed the Shapiro-Wilk to test whether the graph measures were distributed normally. The results showed they did not follow a normal distribution. So the group differences were tested with the non-parametric Wilcoxon rank sum test for independent samples.

Results PLI analysis for younger group In the younger group, for the short distance connectivity analysis, a significant main effect of the group was observed (F[1,118] = 5.480, p = 0.021) in the beta band. As is shown in Figure 2B, it was observed that there is a local increase of connectivity in left parietal region (t = 2.016, p = 0.046) in the delta band, and a local decrease of connectivity in left occipital region (t = -3.043, p = 0.003) in the beta band in children with autism. For the long distance connectivity analysis in the left intra-hemisphere between regions, a significant Group × Region interaction (F[9,1062] = 2.778, p = 0.003) in the delta band was seen. As Figure 2B shows, the ASD group had lower left central-temporal (t = -2.267, p = 0.025) connectivity in the delta band and lower left temporal-parietal and left occipital-parietal connectivity in the alpha band. For the long distance connectivity analysis of the right intra-hemisphere between different regions, a significant main effect of the group (F[1,113] = 1819.202, p<0.001) and a significant Group × Region interaction (F[9,1017] = 7.810, p < 0.001) in the delta band were observed. As Figure 2B shows, the ASD group showed reduced right temporal–parietal (t = -2.040, p=0.043) and reduced right parietal–occipital (t =-1.993, p = 0.048) connectivity in the beta band and higher frontal-parietal (t = 2.247, p = 0.026) connectivity in the gamma band. For the long distance inter-hemispheric connectivity analysis, a significant main effect of the group (F[1,118] = 4.498, p = 0.036) in the beta band was observed. As Figure 2 shows, there was a decrease of long distance connectivity in the left central-right temporal (t = -2.680, p = 0.008), left central-right parietal (t = -2.205, p = 0.029), left temporal-right frontal (t = -2.298, p = 0.023), left central-right central (t = -2.418, p = 0.017), left temporal-right parietal (t = -2.107, p = 0.037), left parietal-right temporal (t = -2.276, p = 0.024), left parietal-right parietal (t = -2.055, p = 0.042), and left parietal-right occipital (t = -2.141, p = 0.034) in children with ASD.

PLI analysis for the older groups In the older groups, for the short distance connectivity analysis, a significant Group × Region interaction (F[9,576] = 2.136, p = 0.025) was seen in the delta band, with a significant main effect of group (F[1,64] = 5.171, p = 0.026) in the theta band, and a significant Group × Region interaction (F[9,576] = 2.030, p = 0.034) in the alpha band. There was a local increase of the right central region (t = 2.131, p = 0.036) in the delta band and a local increase of the left frontal region (t = 2.953, p = 0.004) connectivity in children with ASD. A local increase of connectivity in the right frontal region (t = -2.936, p = 0.004) and the right occipital (t = -2.583, p = 0.012) in the theta band were also observed. In the alpha band, a local increase of the right central region (t = -2.276, p = 0.020) was observed in children with ASD.

For the long distance connectivity analysis, in the right intra-hemisphere between regions, a significant main effect of Group (F[1,64] = 6.209, p = 0.015) was found in the theta band, and a reduced right frontal-central (t = -2.143, p = 0.035), and right parietal-occipital (t = -0.067, p = 0.042) in the theta band were found in children with ASD. In the alpha band, a significant Group × Region interaction (F[9,576] = 2.209, p = 0.020) was found. In the beta band, a

significant Group × Region interaction (F[9,576] = 2.575, p = 0.007) was found. In the gamma band, a significant Group × Region interaction (F[9,576] = 2.661, p = 0.005) was found with a reduced right central-occipital connectivity in children with ASD. For the long distance connectivity analysis in the left intra-hemisphere between regions, a significant Group × Region interaction (F[9,576] = 2.658, p = 0.005) in the alpha band and a significant Group × Region interaction (F[9,576] = 2.990, p = 0.002) in the gamma band were found. A decrease of the left central-parietal (t = -2.526, p = 0.014) in the alpha band and an increase of the left frontal-temporal (t = 2.388, p = 0.019) in the gamma band were observed. For the long-distance inter-hemispheric connectivity analysis, a significant main effect of Group (F[1,64] = 6.527, p = 0.013) in the theta band and a significant Group × Region interaction (F[24,1536] = 3.266, p = 0.001) in the gamma band were found. In the theta band, a decrease of the left temporal-right frontal (t = -2.763, p = 0.007), left temporal-right central (t = -3.019, p = 0.003), left parietal-right frontal (t = -3.173, p = 0.002), and left parietal-right central (t = -3.101, p = 0.002) were found in children with autism. In the gamma band, an increase of the left frontal-right frontal (t = 2.229, p = 0.029), a decrease of the left central-right occipital (t = -3.302, p = 0.001), and a left temporal-right occipital (t = -2.151, p = 0.035) were observed in children with autism.

Figure 2. Schematic illustration of significant differences of connectivity results of phase lag index (PLI) between ASD group and TD group at each of the frequency band. Values represent t values for each comparison of different brain regions. Each panel shows all children, younger children and older children. Squares outlined in white represents significant connectivity between different regions (independent two-sample t-test, p < 0.05). LF: left frontal region; LC: left central region; LT: left temporal region; LP: left parietal region; LO: left occipital region; RF: right frontal region; RC: right central region; RT: right temporal region; RP: right parietal region; RO: right occipital region.

Graph Analysis Difference between Groups

In this study, graph analysis focused on five graph-theoretic measures: clustering coefficient, mean shortest path length, lambda (normalized path length), and gamma (normalized clustering). Considering the network patterns changes that occur during development, both the ASD and TD group were divided into two age bins of younger and older children. The non-parametric Wilcoxon rank sum test was conducted at the 5% significance level to examine the difference between the ASD and TD both in both the younger and older groups as related to network characteristics. These measure the characteristics of network differences between the ASD and TD children, for both younger and older groups at each of the five frequency bands. The results are shown in Table 2.

In the group difference analysis for all children, the clustering coefficient was significantly lower in the ASD group, including all children in the theta (p = 0.023), alpha (p = 0.028) and beta bands (p < 0.001). A significant reduction of mean shortest path length was observed in the ASD group in the gamma band (p = 0.022). The normalized clustering was significantly higher in the ASD group compared to the healthy controls in all bands except the beta band. The children with ASD showed an increased normalized shortest path length at each frequency band. Additionally, small-worldness was significantly decreased in ASD children at each frequency band.

In the group difference analysis with the younger children, the differences between the ASD and TD children appeared band-specific and particularly distinct in the delta and theta bands. The clustering coefficient was significantly lower in the ASD younger group in the beta band (p=0.001). The normalized clustering coefficient was significantly increased in the younger ASD children as compared to the healthy controls. The normalized path length was significantly increased in the younger ASD children in both the delta band and the theta band.

In the group difference analysis with the older children, the clustering coefficient was significantly reduced in the older ASD group in the theta band (p = 0.004) and the alpha band (p < 0.001). The mean shortest path length in the older ASD group was significantly higher in the alpha band (p = 0.040) and significantly lower in the gamma band (p = 0.010). The normalized clustering coefficient was significantly increased in the older ASD children as compared to the matched healthy controls in all sub-frequency bands except the beta band. The normalized path length was significantly increased in all sub-frequency bands. Additionally, small-worldness was significantly decreased in the ASD children at each frequency band.

Table 2. Results of Wilcoxon rank sum test graph properties between ASD and TD groups at each frequency band All children 80 ASD (Age range：3-11 years Age: 5.69 ±2.13 years) vs. 106 TD (Age range：3-11 years Age: 5.85 ± 1.97 years)

Younger children 60 ASD (Age range：3-6 years Age: 4.61 ±1.01 years) vs. 76 TD (Age range：3-6 years Age: 4.81 ± 1.07 years)

P value

P value

Delta

Theta

Alpha

Beta

Cw

0.137

0.023 (↓ ↓)

0.028 (↓ ↓)

Lw

0.502

0.977

Gamma

<0.001 (↑ ↑) <0.001 (↑ ↑) <0.001 (↓ ↓)

0.002 (↑ ↑) <0.001 (↑ ↑) <0.001 (↓ ↓)

Lambda Small-worldne ss

Older children 20ASD (Age range：6-11 years Age: 8.94 ±1.01 years) vs. 40 TD (Age range：6-11 years Age: 8.49 ± 1.03 years) P value

Gamma

Delta

Theta

Alpha

Beta

<0.001 (↓ ↓)

0.276

0.634

0.567

0.470

0.333

0.419

0.244

0.449

0.001 (↑ ↑) 0.009 (↑ ↑） 0.026) (↓ ↓)

0.699

0.022 (↓ ↓) 0.006 (↑ ↑) 0.015 (↑ ↑) 0.023 (↓ ↓)

0.001 (↑ ↑) 0.003 (↑ ↑) 0.091

0.006 (↑ ↑) 0.010 (↑ ↑) 0.079

0.014 (↑ ↑) 0.011 (↓ ↓)

Gamma

Delta

Theta

Alpha

Beta

Gamma

0.001 (↓ ↓)

0.439

0.053

0.004 (↓ ↓)

<0.001 (↓ ↓)

0.162

0.043

0.914

0.186

0.218

0.789

0.389

0.187

0.016 (↑ ↑) 0.058

0.866 0.152

0.046 (↑ ↑) 0.262

0.655

0.806

0.889

0.031 (↑ ↑) 0.004 (↑ ↑) 0.009 (↓ ↓)

0.012 (↑ ↑) 0.004 (↑ ↑) 0.003 (↓ ↓)

0.040 (↑ ↑) 0.014 (↑ ↑) 0.008 (↑ ↑) 0.010 (↓ ↓)

0.010 (↓ ↓) 0.028 (↑ ↑) 0.002 (↑ ↑) 0.003 (↓ ↓)

0.058 0.002 (↑ ↑) 0.002 (↓ ↓)

Results of Wilcoxon rank sum test are shown for clustering (Cw), mean shortest path length (Lw), normalized clustering (gamma), normalized path length (lambda), and small-worldness (SW) for each frequency band. Arrows indicated the direction of the group effect: (↓ ↓), ASDTD. Bold text indicates a statistical significant difference (p<0.05)

Graph topology change with age

Figure 3 reveals the results of group differences between ASD and TD children in the network properties for the different age groups. The age range of the participants included in this study was 3 to 11 years, which is from early childhood to late childhood. This allowed us to examine the impact of age on the development of network properties. As Figures 3A and 3B show, the clustering of the TD group shows a trend of increased clustering with age in the delta band (rho = 0.194, p = 0.046) and in the theta band (rho = 0.250, p = 0.010). However, the ASD group shows an opposite trend in clustering with age in the delta band (rho = -0.323, p = 0.004) and theta band (rho = -0.266, p = 0.018). For the mean shortest path length analysis, the TD group shows a decreasing trend in the theta band (rho = -0.267, p = 0.006), while no such trend was observed in the ASD group, as shown in Figure 3C.

Figure 3. Development of network properties of ASD group (red) and TD group (blue) at each frequency. (A) Scatter plot showing the correlation between age and clustering in the delta band. (B) Scatter plot showing correlation between age and clustering in the theta band. (C) Scatter plot showing significant correlation between age and mean shortest path length in the theta band.

EEG Predictors of Symptoms and Behavior

In this study, a multivariable regression model analysis was conducted for behavior assessment using the ABC Scale, the SRS scale, and the SCQ scale for the ASD group to examine whether the EEG graph indices correlated to ASD symptom severity. In order to study the graph indices in which frequency bands were significantly correlated to symptom severity, we constructed a multivariable regression model with different graph indices in the ranges delta, theta, alpha, beta, and gamma, respectively. Because EEG maturation is known to improve with age, we included age as a continuous covariate of no interest. The model was considered statistically significant when the p value was <0.05 by using an F test. Individual regression coefficients were calculated with t tests. All statistical analysis results were evaluated with SPSS 20.0.

Graph indices: Clustering For the ASD group, a multiple linear regression model was constructed with clustering in the ranges delta, theta, alpha, beta, and gamma as independent variables for each of the dependent variables (the scales and subscales of ABC, SRS and SCQ). As the results show in Table 3, the strongest correlation between the behavioral and graph indices among children with ASD was found for clustering in the alpha band. A negative relationship was found between clustering in the alpha band and the ABC total score, and for the subscales of ABC for sensory behavior and relating behavior. A smaller clustering in the alpha band predicted increased symptom severity on the ABC total scores (t = -2.248, p = 0.031) (Figure 4A), the sensory behavior subscale of ABC (t = -2.032, p = 0.048) (Figure 4C), the relating behavior subscale of ABC (t = -2.451, p = 0.020) (Figure 4E), but not on the body and object use behavior, language behavior, or social and self-help skills subscales.

Table 3. Multiple regression models of ABC scales and subscales and clustering for ASD.

Regression variables Clustering (1-4Hz) Clustering (4-6Hz) Clustering (6-13Hz) Clustering (13-30Hz) Clustering (30-45Hz) Model statistics R2 adj F

ABC

tbeta (standardized coefficients) ABC (S) ABC (R) ABC (B) ABC (L)

ABC (S)

0.099 0.422

-0.006 0.311

0.084 0.437

0.230 0.272

-0.073 0.221

0.209 0.339

-0.339*

-0.296*

-0.348*

-0.268

-0.073

-0.297

-0.048 0.220

0.263 -0.022

0.206 0.029

-0.340 0.006

-0.048 0.302

-0.217 0.381*

0.248 3.571*

0.296 4.283**

0.331 4.859**

0.026 1.211

0.024 1.194

0.211 3.086*

Graph indices: Mean shortest path length

For the ASD group, a multiple linear regression model was constructed with mean shortest path length in the ranges delta, theta, alpha, beta, and gamma as independent variables for each of the dependent variables (the scales and subscales of ABC, SRS and SCQ). As the results show in Table 4, the strongest correlation between the behavioral and graph indices among children with ASD was found for the mean shortest path length in the alpha band. A positive relationship was found between clustering in the alpha band and the ABC total score, and with the subscales of ABC for sensory behavior and relating behavior. A larger mean shortest path length in the alpha band predicted increased symptom severity on the ABC total scores (t = 2.320, p = 0.027) (Figure

4B), and on the sensory behavior subscale of ABC (t = 2.017, p = 0.046) (Figure 4D), and on the relating behavior subscale of ABC (t = 2.482, p = 0.018) (Figure 4E) but not to the body and object use behavior, language behavior, or social and self-help skills subscales.

Table 4. Multiple regression models for ABC scales and subscales and mean shortest path length

for ASD.

Regression variables Mean shortest path length (1-4Hz) Mean shortest path length (4-6Hz) Mean shortest path length (6-13Hz) Mean shortest path length (13-30Hz) Mean shortest path length (30-45Hz) Model statistics R2adj F

ABC

tbeta (standardized coefficients) ABC(S) ABC(R) ABC(B) ABC(L)

ABC(S)

-0.094 -0.315

0.033 -0.282

-0.072 -0.366

-0.210 -0.149

0.048 -0.161

-0.230 -0.235

0.348*

0.300*

0.353*

0.288

0.070

0.305

-0.066 -0.228

-0.260 0.015

-0.093 -0.218

0.223 -0.023

-0.066 -0.294

0.107 -0.403*

0.246 3.550*

0.260 3.736**

0.324 4.733**

0.005 1.037

0.049 1.399

0.209 3.058

Figure 4. Regression plot between the network properties and the ASD symptom severity scores. (A) Scatter plot depicting the relationship between clustering (6-13Hz) and the ABC total scores. (B) Scatter plot depicting the relationship between mean shortest path length in the alpha band (6-13Hz) and the ABC total scores. (C) Scatter plot depicting the relationship between clustering (6-13Hz) and ABC on the sensory stimuli subscale. (D) Scatter plot depicting the relationship between mean shortest path length (6-13Hz) and ABC on the sensory stimuli subscale. (E) Scatter plot depicting the relationship between clustering (6-13Hz) and ABC on the relating behavior subscale. (F) Scatter plot depicting the relationship between mean shortest path length (6-13Hz) and ABC on the relating behavior subscale.

Discussion The aim of the present study was to investigate the connectivity patterns and the topological properties of the brain networks in children with ASD and in typical development children from early childhood to late childhood. For that purpose, we collected a large cross-sectional sample of children ranging in age from 3 to 11 years. Early childhood is a critical stage in physical, cognitive and social-emotional development; in addition, it is frequently a key period for the appearance of ASD symptoms. Our findings suggest an abnormal functional connectivity in both the younger ASD group and in the older ASD group. In the graph analysis, the results revealed abnormal functional network organization in the ASD group. For example, reduced clustering and enhanced path length in specific frequencies were observed in children with ASD in both the younger and older groups. The results indicated that children with ASD may be affected in their early developmental stages. Additionally, the results indicated an age-related increase of clustering and reductions in shortest path length in the brain network from early to late childhood in the typical controls, while the ASD children showed atypical age-related alterations.

The studies about age-related functional connectivity have demonstrated a trend for reduced local connectivity and stronger long distance connectivity during normal development (Hagmann et al., 2010; Uhlhaas et al., 2009). Uhlhass et al. studied the development of neural synchrony in EEG recordings during a Gestalt perception task with participants aged from 6 to 21. Results indicated that age was the only significant predictor for increased neural synchrony during normal development (Uhlhaas et al., 2009). Many studies have suggested that disrupted coordination of local and global synchronization may contribute to ASD. In this study, reduced beta and gamma

band connectivity were reported in the occipital-frontal, occipital-temporal, and occipital-central zones in the ASD group. These results align with the study (Bangel et al., 2014) and suggest that the cognitive deficit prevalent in patients with ASD (L. Sun et al., 2012) may attribute to their diminished neuronal synchrony in the high frequency band and reduced communication among the brain regions. Moreover, reduced long distance connectivity in the slow wave band was in line with the prevailing view that longer range connectivity is mediated by theta oscillations (Khan et al., 2013).

Studies involving age-related network analysis through the employment of graph theoretical methods have discovered that alpha-band electrocortical connectivity becomes increasingly integrated, which was reflected in the shorter characteristic path length in the age range from 7 to

11 years (Miskovic et al., 2015). Our findings demonstrated that the mean shortest path length showed a trend toward decreasing in the theta and beta bands in the typical development group, whereas the ASD group showed the opposite trend in the alpha band relative to the healthy controls, suggesting an altered network organization in children with ASD, even in early childhood. In agreement with a study by Boersma et al. (Boersma et al., 2013), reduced clustering was found in the beta band in a younger ASD group ranged in age from 3 to 6 years. Our findings showing reduced global efficiency in the alpha band and gamma band align with the band-specific altered resting state network development in autism shown in an MEG study with ages form 6 to 21years (Kitzbichler et al., 2015). We also observed group differences in the developmental trajectory in the delta, theta and alpha bands. To sum up, our findings support the interpretation that connectivity strength in autism is affected and even presents as an aberrant organization of connectivity (Boersma et al., 2013).

The present study also demonstrated that the network parameters of the alpha band were the only significant predictors for ASD symptom severity. In this study, ASD symptom severity as measured by the ABC scales was negatively correlated with the cluster coefficient and positively correlated with the mean shortest path length in the alpha band. In previous studies, brain network organization has been associated with cognitive performance and intelligence (Stam et al., 2014; van den Heuvel, Stam, Kahn, & Hulshoff Pol, 2009) and it has been suspected that a disrupted network organization may lead to neurological disorders (L. Rutter et al., 2013; Zeng et al., 2015). Palva et al reported that the alpha phase was correlated with the cyclic modulation of neuronal

excitability that causes biased neuronal and behavioral responses to sensory stimuli (Palva & Palva, 2011). Interestingly, our findings suggest that brain network parameters in the alpha band are closely associated with the ABC subscale: sensory stimuli and relating behavior. It can be interpreted that ASD symptoms may be associated with a disruption in the excitatory/inhibitory balance of neural activity. Furthermore, it has been proven that synchronization in the theta and alpha bands is much involved in the large-scale integration of cortical information(Palva & Palva, 2011). A disrupted brain network in the alpha band means large-scale changes in the functional brain network of ASD people.

The results of the present study have demonstrated that a network analysis of resting EEG may offer an efficient method for monitoring cortical dysfunction correlated to the severity of ASD. The network measures may be a promising tool for identifying autism as early in life as possible, accordingly ensuring that intervention can start as soon as possible. However, several limitations

of the present study should be considered. First, a cross-sectional sample was used in the study, therefore a more dynamic overview of developmental changes in EEG activity could not be observed. The severity of ASD may influence the results. Additional longitudinal follow-up studies including individuals from various age ranges and with varying intellectual functions are expected to be necessary to clarify these controversial points.

Conclusion

In summary, our findings show that autism is associated with an abnormal pattern of connectivity and functional organization of the brain network. The results suggest that an imbalance between information integration and segregation can already exist in the early periods of development. Moreover, our findings suggest that an abnormal and deviating pattern of developmental trajectory in patients with ASD may contribute to the behavioral deficits associated with ASD. The correlation between the graph measures and ASD symptom severity demonstrates that brain network properties may serve as potential indicators assisting in early diagnosis of ASD.

Acknowledgements:

This research was supported by grants from the National Key Research and Development Program of China (2016YFC1306203) and Beijing Municipal Commission of Education.

Conflicts of Interest: The author declares no conflict of interest.

Reference Anagnostou, E., & Taylor, M. J. (2011). Review of neuroimaging in autism spectrum disorders: what have we learned and where we go from here. Mol Autism, 2(1), 4. doi: 10.1186/2040-2392-2-4 Association, A. P. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington: VA: American Psychiatric Publishing. Bangel, K. A., Batty, M., Ye, A. X., Meaux, E., Taylor, M. J., & Doesburg, S. M. (2014). Reduced beta band connectivity during number estimation in autism. Neuroimage Clin, 6, 202-213. doi: 10.1016/j.nicl.2014.08.020 Barttfeld, P., Wicker, B., Cukier, S., Navarta, S., Lew, S., & Sigman, M. (2011). A big-world network in ASD: dynamical connectivity analysis reflects a deficit in long-range connections and an excess of short-range connections. Neuropsychologia, 49(2), 254-263. Bassett, D. S., & Bullmore, E. (2006). Small-world brain networks. The neuroscientist, 12(6), 512-523.

Bathelt, J., O'Reilly, H., Clayden, J. D., Cross, J. H., & de Haan, M. (2013). Functional brain network organisation of children between 2 and 5 years derived from reconstructed activity of cortical sources

of

high-density

EEG

recordings.

Neuroimage,

82,

595-604.

doi:

10.1016/j.neuroimage.2013.06.003 Boersma, M., Kemner, C., de Reus, M. A., Collin, G., Snijders, T. M., Hofman, D., Buitelaar, J. K., Stam, C. J., & van den Heuvel, M. P. (2013). Disrupted functional brain networks in autistic toddlers. Brain Connect, 3(1), 41-49. doi: 10.1089/brain.2012.0127 Boersma, M., Smit, D. J., de Bie, H. M., Van Baal, G. C., Boomsma, D. I., de Geus, E. J., Delemarre-van de Waal, H. A., & Stam, C. J. (2011). Network analysis of resting state EEG in the developing young brain: structure comes with maturation. Hum Brain Mapp, 32(3), 413-425. doi: 10.1002/hbm.21030 Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci, 10(3), 186-198. doi: 10.1038/nrn2575 Chechik, G., Meilijson, I., & Ruppin, E. (1998). Synaptic pruning in development: a computational account. Neural Comput, 10(7), 1759-1777. Clancy, H., Dugdale, A., & Rendle-Short, J. (1969). The diagnosis of infantile autism. Dev Med Child Neurol, 11(4), 432-442. Coben, R., Clarke, A. R., Hudspeth, W., & Barry, R. J. (2008). EEG power and coherence in autistic spectrum

disorder.

Clinical

Neurophysiology,

119(5),

1002-1009.

doi:

10.1016/j.clinph.2008.01.013 Constantino, J. N., & Gruber, C. P. (2005). Social Responsiveness Scale (SRS). Los Angeles: Western Psychological Services. Cragg, L., Kovacevic, N., McIntosh, A. R., Poulsen, C., Martinu, K., Leonard, G., & Paus, T. (2011). Maturation of EEG power spectra in early adolescence: a longitudinal study. Dev Sci, 14(5), 935-943. doi: 10.1111/j.1467-7687.2010.01031.x Dawson, G., Webb, S., Schellenberg, G. D., Dager, S., Friedman, S., Aylward, E., & Richards, T. (2002). Defining the broader phenotype of autism: Genetic, brain, and behavioral perspectives. Dev Psychopathol, 14(03), 581-611. Dean, D. C., 3rd, O'Muircheartaigh, J., Dirks, H., Waskiewicz, N., Lehman, K., Walker, L., Han, M., & Deoni, S. C. (2014). Modeling healthy male white matter and myelin development: 3 through 60months of age. Neuroimage, 84, 742-752. doi: 10.1016/j.neuroimage.2013.09.058 Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods, 134(1), 9-21. Durka, P. J., Klekowicz, H., Blinowska, K. J., Szelenberger, W., & Niemcewicz, S. (2003). A simple system for detection of EEG artifacts in polysomnographic recordings. IEEE Trans Biomed Eng, 50(4), 526-528. doi: 10.1109/TBME.2003.809476 Eaves, L. C., Wingert, H. D., Ho, H. H., & Mickelson, E. C. (2006). Screening for autism spectrum disorders with the social communication questionnaire. J Dev Behav Pediatr, 27(2 Suppl), S95-S103. Frohlich, J., Irimia, A., & Jeste, S. S. (2015). Trajectory of frequency stability in typical development. Brain Imaging Behav, 9(1), 5-18. doi: 10.1007/s11682-014-9339-3 Ghanbari, Y., Bloy, L., Christopher Edgar, J., Blaskey, L., Verma, R., & Roberts, T. P. (2015). Joint analysis of band-specific functional connectivity and signal complexity in autism. J Autism Dev Disord, 45(2), 444-460. doi: 10.1007/s10803-013-1915-7

Hagmann, P., Sporns, O., Madan, N., Cammoun, L., Pienaar, R., Wedeen, V. J., Meuli, R., Thiran, J. P., & Grant, P. E. (2010). White matter maturation reshapes structural connectivity in the late developing human brain. Proc Natl Acad Sci U S A, 107(44), 19067-19072. doi: 10.1073/pnas.1009073107 Humphries, M. D., Gurney, K., & Prescott, T. J. (2006). The brainstem reticular formation is a small-world, not scale-free, network. Proceedings of the Royal Society of London B: Biological Sciences, 273(1585), 503-511. Jamal, W., Das, S., Oprescu, I. A., Maharatna, K., Apicella, F., & Sicca, F. (2014). Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates. J Neural Eng, 11(4), 046019. doi: 10.1088/1741-2560/11/4/046019 Just, M. A., Keller, T. A., Malave, V. L., Kana, R. K., & Varma, S. (2012). Autism as a neural systems disorder: a theory of frontal-posterior underconnectivity. Neuroscience & Biobehavioral Reviews, 36(4), 1292-1313. Khan, S., Gramfort, A., Shetty, N. R., Kitzbichler, M. G., Ganesan, S., Moran, J. M., Lee, S. M., Gabrieli, J. D., Tager-Flusberg, H. B., & Joseph, R. M. (2013). Local and long-range functional connectivity is reduced in concert in autism spectrum disorders. Proceedings of the National Academy of Sciences, 110(8), 3107-3112. Kitzbichler, M. G., Khan, S., Ganesan, S., Vangel, M. G., Herbert, M. R., Hamalainen, M. S., & Kenet, T. (2015). Altered development and multifaceted band-specific abnormalities of resting state networks in autism. Biol Psychiatry, 77(9), 794-804. doi: 10.1016/j.biopsych.2014.05.012 Matlis, S., Boric, K., Chu, C. J., & Kramer, M. A. (2015). Robust disruptions in electroencephalogram cortical oscillations and large-scale functional networks in autism. BMC Neurol, 15, 97. doi: 10.1186/s12883-015-0355-8 Menon, V. (2011). Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci, 15(10), 483-506. doi: 10.1016/j.tics.2011.08.003 Minshew, N., Sweeney, J., & Luna, B. (2002). Autism as a selective disorder of complex information processing and underdevelopment of neocortical systems. Mol Psychiatry, 7(s2), S14-S14. Miskovic, V., Ma, X., Chou, C. A., Fan, M., Owens, M., Sayama, H., & Gibb, B. E. (2015). Developmental changes in spontaneous electrocortical activity and network organization from early to late childhood. Neuroimage, 118, 237-247. doi: 10.1016/j.neuroimage.2015.06.013 Murias, M., Webb, S. J., Greenson, J., & Dawson, G. (2007). Resting state cortical connectivity reflected in EEG coherence in individuals with autism. Biol Psychiatry, 62(3), 270-273. doi: 10.1016/j.biopsych.2006.11.012 Palva, S., & Palva, J. M. (2011). Functional roles of alpha-band phase synchronization in local and large-scale cortical networks. Front Psychol, 2, 204. doi: 10.3389/fpsyg.2011.00204 Rutter, L., Nadar, S. R., Holroyd, T., Carver, F. W., Apud, J., Weinberger, D. R., & Coppola, R. (2013). Graph theoretical analysis of resting magnetoencephalographic functional connectivity networks. Front Comput Neurosci, 7, 93. doi: 10.3389/fncom.2013.00093 Rutter, M., Bailey, A., & Lord, C. (2003). The social communication questionnaire. Los Angeles: Western Psychological Services. Rutter, M., Bailey, A., Lord, C., Cianchetti, C., & Fancello, G. S. (2007). SCQ: Social Communication Questionnaire: Manuale: Giunti OS. Ryland, H. K., Hysing, M., Posserud, M. B., Gillberg, C., & Lundervold, A. J. (2012). Autism spectrum symptoms in children with neurological disorders. Child Adolesc Psychiatry Ment Health, 6(1),

34. doi: 10.1186/1753-2000-6-34 Sporns, O. (2014). Contributions and challenges for network models in cognitive neuroscience. Nat Neurosci, 17(5), 652-660. doi: 10.1038/nn.3690 Stam, C. J. (2014). Modern network science of neurological disorders. Nat Rev Neurosci, 15(10), 683-695. doi: 10.1038/nrn3801 Stam, C. J., Nolte, G., & Daffertshofer, A. (2007). Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum Brain Mapp, 28(11), 1178-1193. doi: 10.1002/hbm.20346 Stam, C. J., Tewarie, P., Van Dellen, E., van Straaten, E. C., Hillebrand, A., & Van Mieghem, P. (2014). The trees and the forest: Characterization of complex brain networks with minimum spanning trees. Int J Psychophysiol, 92(3), 129-138. doi: 10.1016/j.ijpsycho.2014.04.001 Sun, L., Grutzner, C., Bolte, S., Wibral, M., Tozman, T., Schlitt, S., Poustka, F., Singer, W., Freitag, C. M., & Uhlhaas, P. J. (2012). Impaired gamma-band activity during perceptual organization in adults with autism spectrum disorders: evidence for dysfunctional network activity in frontal-posterior

cortices.

J

Neurosci,

32(28),

9563-9573.

doi:

10.1523/JNEUROSCI.1073-12.2012 Sun, X., Allison, C., Auyeung, B., Matthews, F. E., Baron-Cohen, S., & Brayne, C. (2013). What is available for case identification in autism research in mainland China? Research in Autism Spectrum Disorders, 7(5), 579-590. Tijms, B. M., Wink, A. M., de Haan, W., van der Flier, W. M., Stam, C. J., Scheltens, P., & Barkhof, F. (2013). Alzheimer's disease: connecting findings from graph theoretical studies of brain networks. Neurobiol Aging, 34(8), 2023-2036. doi: 10.1016/j.neurobiolaging.2013.02.020 Uhlhaas, P. J., Roux, F., Singer, W., Haenschel, C., Sireteanu, R., & Rodriguez, E. (2009). The development of neural synchrony reflects late maturation and restructuring of functional networks

in humans.

Proc Natl

Acad Sci U S A, 106(24), 9866-9871. doi:

10.1073/pnas.0900390106 van den Heuvel, M. P., Stam, C. J., Kahn, R. S., & Hulshoff Pol, H. E. (2009). Efficiency of functional brain networks

and

intellectual

performance.

J

Neurosci,

29(23),

7619-7624.

doi:

10.1523/JNEUROSCI.1443-09.2009 Vertes, P. E., & Bullmore, E. T. (2015). Annual research review: Growth connectomics--the organization and reorganization of brain networks during normal and abnormal development. J Child Psychol Psychiatry, 56(3), 299-320. doi: 10.1111/jcpp.12365 Volkmar, F. R., Cicchetti, D. V., Dykens, E., Sparrow, S. S., Leckman, J. F., & Cohen, D. J. (1988). An evaluation of the Autism Behavior Checklist. J Autism Dev Disord, 18(1), 81-97. Wang, J., Barstein, J., Ethridge, L. E., Mosconi, M. W., Takarae, Y., & Sweeney, J. A. (2013). Resting state EEG abnormalities in autism spectrum disorders. J Neurodev Disord, 5(1), 24. doi: 10.1186/1866-1955-5-24 Zablotsky, B., Black, L. I., Maenner, M. J., Schieve, L. A., & Blumberg, S. J. (2015). Estimated Prevalence of Autism and Other Developmental Disabilities Following Questionnaire Changes in the 2014 National Health Interview Survey. Natl Health Stat Report(87), 1-20. Zeng, K., Wang, Y., Ouyang, G., Bian, Z., Wang, L., & Li, X. (2015). Complex network analysis of resting state EEG in amnestic mild cognitive impairment patients with type 2 diabetes. Front Comput Neurosci, 9, 133. doi: 10.3389/fncom.2015.00133

Highlights 1. A large number of children including 80 ASD and 106 controls participated in the study. 2. Reduced clustering coefficient and enhanced shortest path length in specific frequency was observed in autistic children. 3. An atypical alteration of network properties in the developmental trajectory was observed in autistic children. 4. Graph measures were found to correlate with ASD symptom severity in the alpha band.