EEG resting-state functional connectivity: evidence for an imbalance of external/ internal information integration in Autism

doi:10.21203/rs.3.rs-1107850/v1

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EEG resting-state functional connectivity: evidence for an imbalance of external/ internal information integration in Autism

https://doi.org/10.21203/rs.3.rs-1107850/v1

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Background

Autism spectrum disorder (ASD) is associated with atypical neural activity in resting-state. Most of the studies have focused on abnormalities in alpha-frequency, as a marker of ASD dysfunctions. However, few have explored alpha synchronization, with a specific interest in resting-state networks: the default mode network (DMN), the sensorimotor network (SMN), and the dorsal attention network (DAN). These functional connectivity analyses provide relevant insight into the neurophysiological correlates of multimodal integration in ASD.

Methods

Using the high temporal resolution of EEG, the present study investigates the functional connectivity in the alpha band within and between the DMN, SMN, and the DAN. We examined eyes-closed EEG alpha lagged phase synchronization, using standardized Low-Resolution Brain Electromagnetic Tomography (sLORETA) in 29 participants with ASD and 38 age,- sex- and IQ-matched typically developing (TD) controls.

Results

We observed reduced functional connectivity in the ASD group relative to TD controls, within and between the DMN, the SMN, and the DAN. We identified three hubs of dysconnectivity in ASD: the posterior cingulate cortex, the precuneus, and the medial frontal gyrus. These three regions also presented decreased current source density in the alpha band.

Conclusion

These results may account for impairments in multimodal - sensory and internal information - integration frequently observed in ASD. Underconnectivity potentially involves difficulties switching between this externally oriented attention and internally oriented thoughts and, more broadly, may impact embodied cognition.

Neurology

Pathology

Cognitive Neuroscience

Autism spectrum disorder

EEG

alpha

DMN

DAN

SMN

connectivity

integration

resting-state

Autism spectrum disorder (ASD) refers to neurodevelopmental disorders characterized by social communication difficulties and restricted and repetitive behaviors (1). Studies using functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) at rest have consistently demonstrated atypical functional connectivity in ASD that goes some way to explaining its symptoms (see 2–4, for a review). Resting-state (RS) allows the brain to integrate and prepare for changes in external and internal environments. Thus, atypical functional connectivity at rest in ASD may indicate disintegration of the information through the brain.

In this context, alpha oscillation is an interesting marker of dysfunctions (5). Alpha oscillation (7.5–12.5 Hz) is the main rhythm in the RS, and it decreases when tasks are being performed. These alpha rhythms seem to act as a traffic controller of information flow within the cortex (6). They are thought to be an index of cortical inhibition (7, 8) and probably reflect top-down inhibitory control through modulation of the neural excitation/inhibition balance (6). Alpha oscillation is inversely related to perception and attention, reflecting functional inhibition of sensory systems (9). Alpha suppression has been consistently linked to increased attention and vigilance (10, 11).

Studies exploring brain connectivity have reported decreased connectivity across alpha band in ASD (12–21). Mathewson and colleagues' (22) group analysis showed that reduced coherence in the posterior regions was associated with preferential attention to detail in ASD groups (as measured by the Autism spectrum Quotient, AQ). Impaired neural synchrony may be a primary pathophysiological mechanism in ASD, contributing to atypical functional connectivity (23, 24), and could represent an endophenotype that underlies the information processing impairments in ASD (25, 26). Most of the connectivity findings have been based on measurements between pairs of electrodes, which have several limitations, including volume conduction (27) and poor estimation of anatomical locations (presumption of a 2- and not 3-dimensional space). 3D analyses of EEG sources and connectivity may provide more detailed and accurate information. However, most studies in ASD have focused their EEG connectivity investigations on scalp measurements. One single case study in ASD, using correlation analyses between current source density measure (implemented by standardized Low-Resolution Brain Electromagnetic Tomography (sLORETA)), showed increased short connectivity, between regions involved in the mirror neuron system and social perceptual networks (28). Finally, one MEG study used source localization approach and focused on RS networks (RSNs) in ASD, including the default mode network (DMN) and salience network (29). Results showed lower gamma-band connectivity within the DMN and between the DMN and salience network, correlated with lower social communication and interaction abilities. The DMN is involved in self-reflection, referential thinking, perspective-taking, and autobiographical memory (30, 31). Altered activation and connectivity within the DMN in ASD observed with fMRI have emerged as a key system underlying social dysfunction (see 32 for review).

In the same way, while a growing interest over the last decade concerns atypical sensory processing - a new diagnostic criterion of ASD includes in the DSM-5 (1) – few studies have focused on the sensorimotor network (SMN). Atypical sensorimotor and perceptual processes suggest an impaired SMN (33–35) that is linked to the core symptoms of ASD (36–39).

Furthermore, although attentional atypicality is well known in ASD, very few studies have explored this population's attentional networks. The dorsal attention network (DAN) is concerned with the regulation of goal-directed top-down processing (40). Dysconnectivity in this network may explain attentional process difficulties in ASD (41, 42).

Most RS studies in ASD have been conducted using fMRI, and few have focused on the RSNs in EEG. Exploring the connectivity of the alpha band is of particular interest as it is related to the coordination of distant brain regions (6, 43, 44). The alpha band is positively related to the DMN and the somatosensory system (45–47), and negatively correlated with the DAN (44). Thus, these approaches offer new insights into the mechanisms underlying the atypical functional organization of brain networks in ASD (e.g., high temporal resolution, specific oscillatory frequency-band effects, a direct measure of brain activity) that fMRI cannot reveal (48, 49).

Atypicality has been reported within these RSNs, but data are very sparse concerning connectivity between them in ASD (29, 50, 51). Between-network connectivity reflects the integration of information between different networks, which is crucial for many functions impacted in ASD, such as perception, social interaction, and communication (52, 53).

Taken together, study RS brain communication through alpha rhythms provide a scaffold for low- and high-level cognitive processes. The present study aimed to identify functional connectivity modifications within and between RSNs in the alpha band in ASD compared to typically developing (TD) participants. We expected to observe underconnectivity both within and between the DMN, the DAN and the SMN in the ASD group, indicating poor brain communication. This reduced functional connectivity may reflect the existence of a core multimodal integration deficit affecting bottom-up processes (sensory processing) with significant consequences on internal representations.

2.1. Participants

We recruited 29 participants with ASD and 38 TD controls (mean 16.1 and 16.5 years respectively) via two autism resource centers in France. All participants were right-handed and matched for sex, age, and intellectual quotient (Table 1). Because of the difficulty in finding ASD participants, we decided to keep a large range of ages across participants. Furthermore, to keep a homogenous age range between groups, we seek to find an age-matched control for each ASD participant recruited. These participants with ASD had received a clinical diagnosis of verbally and intellectually high-functioning autism according to DSM-5 criteria (1). The diagnosis was confirmed by administering the Autism Diagnostic Interview-Revised (54) and/or the Autism Diagnostic Observation Schedule-Generic (ADOS, 55), complemented by the AQ (56), except for one participant. For all participants, exclusion criteria were a history of neurological disorders or psychiatric illness (other than ASD in the ASD group), a first-degree relative with ASD in the TD group, head trauma, medication interfering with the EEG signal, intellectual disability, and learning disabilities. All participants voluntarily took part in the study, and written consent was obtained from children and their parents after providing them with detailed information. This research was undertaken following the Declaration of Helsinki and approved by the regional ethics committee (CPP Nord Ouest III). It was supported by the French Ministry of Health (PHRC, ID-RCB: 2014-A00481-46). Each participant underwent a Wechsler Intelligence Scale: the Wechsler Intelligence Scale for Children-Fourth Edition for children aged 6–17 years and the Wechsler Adult Intelligence Scale-Fourth Edition for participants aged 18 years or above.

Table 1

**Population description.** Mean, standard deviation (SD), range, and analyses for group differences (Student’s t-test) for age, Full-Scale Intelligence Quotient (FSIQ), and Autistic Quotient (AQ).
	ASD (n = 29)		TD (n = 38)		Group Comparison
	Mean	SD	Mean	SD	Group Comparison
Age (years)	16.1	3.6	16.5	4.2	ns
FSIQ	99.1	14.0	104.8	10.2	ns
AQ total*	34.3	9.4	11.4	5.0	<.0001

2.2. EEG recording and data acquisition

Continuous EEG recording and data acquisition procedures were similar across research centers. They were performed in a faraday cage for 3 minutes in an awake RS eyes-closed condition, using an EGI Hydrocel Geodesic Sensor Net (HGSN-130) dense array of 128 Ag/AgCl sensors (57) (Electrical Geodesics Inc., Eugene, OR, USA). Participants were instructed to relax, remain as still as possible, and close their eyes. Impedances were kept under 100 kΩ (58), and the EEG channel was referenced to a vertex reference (fixed by the EGI system). EEG data were processed offline using Netstation 4.4.2 (Electrical Geodesics Inc., Eugene, OR, USA) before they were exported, given that we used EEG processing software developed in our laboratory (see the detailed method in 59). The signal was sampled at 1 kHz and filtered using a low-pass filter at 500 Hz and a high-pass filter at 1 Hz. The EEG was then visually screened by two experts (the first authors, PW and PC). The remaining artifacts (e.g., saccades, muscle contractions) were removed before re-referenced to a common average reference.

After processing, nonoverlapping 2.048-s segments were extracted for analysis, and all participants had a minimum of 40 s of artifact-free data available for analysis, respecting Pascual-Marqui et al. (2011)’s guidelines (60). We focused our spectral analysis on the alpha frequency band (7.512.5 Hz) given our hypothesis.

2.1. Source localization analysis and statistical non-parametric mapping (SNPM)

Using sLORETA, we estimated the current density recordings of source signals in a standardized brain atlas space, utilizing a restricted inverse solution (for details, see 61,62). Current source density distributions in the ASD and TD groups were compared in a voxel-by-voxel analysis of sLORETA data for the alpha frequency band. We submitted the sLORETA images to statistical non-parametric mapping (SnPM) for each contrast, applying a t-statistic to log-transformed data for unpaired groups. We log-transformed sLORETA images before the statistical analyses for each participant to reduce confounds with no regional specificity (63). Correction for multiple comparisons was applied in SnPM with random permutations (5000 in the current study) and has been shown to yield results similar to those obtained from statistical parametric mapping with a general linear model and multiple comparison corrections derived from random field theory (64, 65). A t-threshold (t = 3.426) corresponding to a statistical significance threshold (p < 0.05) was calculated using the statistical tool provided by sLORETA. Only regions with at least five significant voxels were retained.

2.2. Functional Connectivity analysis

2.2.1. Regions of interest

To evaluate between-group connectivity differences, we conducted analyses with different RSNs. We selected seeds from key regions within the SMN, DMN, and DAN, from the parcellations described by Wu et al., (66) and Yeo et al. (67), resulting in five regions of interest (ROIs) for the SMN, four for the DMN, and twelve for the DAN. SMN coordinates were transformed from Talairach to MNI coordinates.

ROIs were created by including all gray-matter voxels within a 10-mm radius of the seed. The log-transformed electric current density was averaged across all voxels belonging to a given ROI. LORETA gave the names of all the regions to the corresponding coordinates (listed in Table 2). For DAN, two voxels were each labeled precuneus right by LORETA nomenclature. As one of them was very close to the right Superior parietal, we labeled it precuneus-superior parietal lobule R to differentiate it from the second voxel located more in the center of the precuneus.

Table 2

**Cortical regions of interest for RSN functional connectivity.** SMN: sensorimotor network; DAN: dorsal attention network; DMN: default mode network; L: left, R: right.
RSNs	Regions	MNI coordinates
RSNs	Regions	X	Y	Z
SMN	Precentral gyrus R	34	-32	59
	Postcentral gyrus L	-44	-30	58
	Medial frontal gyrus	-4	-33	66
	Transverse temporal gyrus L	-56	-23	12
	Transverse temporal gyrus R	55	-23	12
DAN	Middle frontal gyrus R	22	-8	54
	Middle frontal gyrus L	-22	-8	54
	Inferior parietal lobule R	34	-38	44
	Inferior parietal lobule L	-34	-38	44
	Precuneus R	18	-69	51
	Precuneus L	-18	-69	51
	Middle temporal gyrus R	51	-64	-2
	Middle temporal gyrus L	-51	-64	-2
	Precuneus - Superior parietal lobule R	8	-63	57
	Superior parietal lobule L	-8	-63	57
	Inferior frontal gyrus R	49	3	34
	Inferior frontal gyrus L	-49	3	34
DMN	Superior temporal gyrus R	41	-60	29
	Superior temporal gyrus L	-41	-60	29
	Medial frontal gyrus	0	49	18
	Posterior cingulate cortex	0	-52	26

2.2.2. Functional Connectivity Analyses

The connectivity analyses were performed by computing lagged phase synchronization using sLORETA. Lagged phase synchronization provided the similarity (a corrected phase synchrony value) between signals in the frequency domain based on normalized Fourier transforms. This measure corrects the volume conduction effects, intrinsic physics artifacts or non-physiological effects, and low spatial resolution. It represents a non-linear connectivity measure between two regions after excluding this zero-lag contribution (for more details on the sLORETA connectivity algorithm, see 60). In this respect, it is considered to contain only physiological connectivity (48).

We examined group differences on within- and between-network connectivity by comparing lagged phase synchronization between ROIs for each artifact-free EEG segment in the alpha frequency band. Analyses were conducted using a one-tailed t statistic on log-transformed data corrected for multiple comparisons with a non-parametric permutation procedure (5000 randomizations). The t thresholds corresponding to statistical significance thresholds (p < 0.05) were calculated for connectivity analyses within the SMN (t = 2.604), DAN (t = 3.127) and DMN (t = 2.412), and for differences in connectivity between the SMN and DAN (t = 3,343), SMN and DMN (t = 3.019), and DAN and DMN (t = 3.291).

3.1. Source Localization

In the ASD group, relative to the TD group, source localization analyses in the alpha band showed significantly decreased current source density. Reduced current source density was localized in temporoparietal and somatosensory/medial areas, in the main regions involved in the DMN, the SMN, and the DAN (Fig. 1, see Additional file 1). No difference was found for ASD > TD.

3.2. Functional connectivity within RSNs

We performed statistical analyses with sLORETA software to assess between-group differences in lagged phase synchronization within the three RSNs, namely the SMN, DAN, and DMN (Table 3, Fig. 2 and summary Fig. 4). No difference was found for ASD > TD. Within the SMN, we identified significantly reduced connectivity between the medial frontal gyrus and right precentral gyrus in the ASD group relative to the TD group. Within the DAN, statistical analyses revealed reduced connectivity in the ASD group relative to the TD group 1) between the right precuneus- superior parietal lobule and both the right middle frontal gyrus and the right inferior parietal lobule, 2) between the left precuneus and both the right middle temporal gyrus and the right inferior frontal gyrus, 3) between the right middle temporal gyrus and the right inferior frontal gyrus, and 4) between the right middle frontal gyrus and the left superior parietal lobule. Within the DMN, we identified significantly decreased connectivity between the posterior cingulate cortex and the right and left superior temporal gyrus in the ASD group relative to the TD group.

Table 3

**Significant differences within-RSN functional connectivity in the ASD group relative to the TD group**. SMN: sensorimotor network; DAN: dorsal attention network; DMN: default mode network; L: left, R: right.
Regions	MNI Coordinates			Regions	MNI Coordinates			t	p
Regions	x	y	z		x	y	z
SMN
Medial frontal gyrus	-4	-33	66	Precentral gyrus R	34	-32	59	-3.555	<0.005
DAN
Precuneus-Superior parietal lobule R	8	-63	57	Middle frontal gyrus R	22	-8	54	-3.817	<0.009
Precuneus L	-18	-69	51	Middle temporal gyrus R	51	-64	-2	-3.764	<0.011
Precuneus L	-18	-69	51	Inferior frontal gyrus R	49	3	34	-3.503	<0.020
Middle temporal gyrus R	51	-64	-2	Inferior frontal gyrus R	49	3	34	-3.315	<0.032
Precuneus-Superior parietal lobule R	8	-63	57	Inferior parietal lobule R	34	-38	44	-3.247	<0.038
Middle frontal gyrus R	22	-8	54	Superior parietal lobule L	-8	-63	57	-3.160	<0.046
DMN
Posterior cingulate cortex	0	-52	26	Superior temporal gyrus L	41	-60	29	-2.892	<0.018
Posterior cingulate cortex	0	-52	26	Superior temporal gyrus R	-41	-60	29	-2.559	<0.038

3.3. Functional connectivity between RSNs

The ASD and TD groups also differed on connectivity between RSNs (Table 4, Fig. 3 and summary in Fig. 4). No difference was found for ASD > TD. For lagged phase synchronization between SMN and DAN, we identified significantly decreased connectivity in the ASD group relative to the TD group 1) between the medial frontal gyrus of the SMN and both the left superior parietal lobule and the bilateral Precuneus of the DAN, 2) between the left transverse temporal gyrus of the SMN and the right Precuneus (Superior parietal) of the DAN, and 3) between the right precentral gyrus of the SMN and the right inferior parietal lobule of the DAN. For SMN and DMN, we identified significantly decreased connectivity in the ASD group relative to the TD group between the medial frontal gyrus of the SMN and the right superior temporal gyrus of the DMN. For DAN and DMN connectivity, results revealed significantly decreased lagged phase synchronization in the ASD group relative to the TD group between the right Precuneus (Superior parietal) of the DAN and both the left superior temporal gyrus and the posterior cingulate cortex of the DMN.

Table 4

**Significant differences between-RSN functional connectivity in the ASD group relative to the TD group**. SMN: sensorimotor network; DAN: dorsal attention network; DMN: default mode network; L: left, R: right.
Regions	MNI Coordinates			Regions	MNI Coordinates			t	p
Regions	x	y	z		x	y	z
SMN & DAN SMN				DAN
Medial frontal gyrus	-4	-33	66	Superior parietal lobule L	-8	-63	57	-3.667	<0.025
Transverse temporal gyrus L	-56	-23	12	Precuneus-Superior parietal lobule R	8	-63	57	-3.640	<0.026
Medial frontal gyrus	-4	-33	66	Precuneus R	18	-69	51	-3.611	<0.028
Precentral gyrus R	34	-32	59	Inferior parietal lobule R	34	-38	44	-3.561	<0.032
Medial frontal gyrus	-4	-33	66	Precuneus L	-18	-69	51	-3.459	<0.038
SMN & DMN SMN				DMN
Medial frontal gyrus	-4	-33	66	Superior temporal gyrus R	41	-60	29	-3.529	<0.015
DAN & DMN DAN				DMN
Precuneus-Superior parietal lobule R	8	-63	57	Superior temporal gyrus L	-41	-60	29	-3.460	<0.033
Precuneus-Superior parietal lobule R	8	-63	57	Posterior cingulate cortex	0	-52	26	-3.293	<0.05

The present study investigated sources and functional connectivity in RSN in the alpha frequency band, comparing participants with and without ASD, using lagged phase synchronization. We observed decreased connectivity within and between the DMN, the SMN, and the DAN in the ASD group compared to the TD group. We highlighted three principal hubs of dysconnectivity: the posterior cingulate cortex, the precuneus, and the medial frontal gyrus. We also showed reduced current source density in the alpha band mainly located in temporoparietal and somatosensory/medial areas considered as key nodes in the DMN, SMN and DAN.

4.1. Alpha band: a relevant marker for studying ASD atypicality

The alpha band is involved in controlling and regulating the information flow, both within and between brain networks, as it selectively inhibits task-irrelevant pathways (6, 68, 69). Alpha activity seems to be associated with modulating sensory input (68, 70, 71). Alpha disturbances could lead to atypical sensory processing in ASD (72, 73). Given together, this might suggest that individuals with ASD would have more difficulty integrating incoming information (bottom-up) at rest and actively inhibit their sensitivity to sensory input.

Increasing evidence also shows that alpha activity plays a role in abnormal social and emotional stimuli processing in ASD (74, 75). Given the alpha band involvement in attention and perception (lower-level processes) and social cognition (high-level process) (5, 6, 22, 76), we speculated that alpha atypicality contributes to autistic symptoms. Indeed, Shepard ((77) recently showed that children with ASD who exhibit greater reductions in alpha power have more social communication difficulties.

In their review, Wang et al. (4) proposed that decreased alpha band may be related to an imbalance of excitatory (e.g., glutamatergic) and inhibitory (e.g., GABAergic) activity in ASD (also see 75,78). In particular, alpha desynchronization may be linked to increased neural excitability/decreased neural inhibition (6). Previous studies in ASD reported abnormal cortical inhibitory interneurons (79, 80) and altered glutamatergic levels (81, 82), associated with atypical sensory processing in ASD (73, 83). The excitation/inhibition imbalance increases neural noise in ASD and may lead to atypical sensory processing and under-responsiveness to behaviorally relevant stimuli in participants with ASD (72).

Our data points out that excitation/inhibition imbalance highlighted by a decrease in alpha activity increases neural noise in ASD. This seems to impact integration difficulties affecting both low-level processes (sensory processing) and high-level processes, with an effect on the representation of oneself in the world and the shifting of mental representations (84–86).

4.2. Functional connectivity within resting-state networks

We revealed reduced lagged phase synchronization within and between the SMN, DAN, and DMN in the ASD group compared to the TD group, possibly reflecting an imbalance between external and internal information integration.

First, participants with ASD exhibited decreased connectivity within the DMN, involving the posterior cingulate cortex and right and left superior temporal gyrus, as previously reported in fMRI studies (32, 87–89) and MEG studies for the gamma band (29). A study revealed that underconnectivity involving the posterior cingulate cortex, insula, and superior temporal gyrus was negatively associated with ASD symptom severity measured by ADOS (90). These DMN perturbations may profoundly modify internal information, including memory, mental simulation, or the sense of self, and act as a significant contributor to social dysfunction (see 32 for review).

Second, we highlighted underconnectivity within the SMN, between the medial frontal gyrus and right precentral gyrus. A recent fMRI study also showed reduced functional connectivity among visual association, somatosensory, and motor networks (91). This decreased functional connectivity between somatosensory and motor networks is consistent with the atypical multisensory and motor integration observed in individuals with ASD and participate in the core symptoms of ASD (92, 93). Dysconnectivity within the SMN may participate in an atypical integration of the sensory environment and might be interpreted as an index of increased sensitivity to sensory information according to the Enhanced Perceptual Functioning theory (94). Individuals with ASD may have more difficulty gate incoming information (bottom-up processing) at rest. Therefore, multisensory and motor integration plays a crucial role in developing imitation, motor communication, and social skills (95, 96), all impaired in ASD.

Finally, as previously reported in fMRI by Bi et al. (41) and Farrant & Uddin (42), we found dysconnectivity within several DAN regions for participants with ASD relative to the TD group. We observed the main underconnectivity between the right precuneus-superior parietal lobule and right middle frontal gyrus, consistent with deficits in attention regulation and shifting between external and internal stimuli that may directly impact self-information integration. More broadly, as the DAN is involved in regulating exogenous goal-directed top-down processing (40), we hypothesized that this atypical connectivity might contribute to the difficulties in arousal regulation and shifting associated with autistic symptoms (97).

4.3. Functional connectivity between resting-state networks

The underconnectivity observed in the ASD group between the SMN and DAN may generate delays in perceptual input and information transmission to systems downstream, thereby compromising information integration (31, 98). The attentional focus seems to be less oriented to the external information, especially when directly related to own’s body representation. It may help to explain atypical functioning in the form of detail-focused perception (99).

Moreover, the underconnectivity we observed between DAN (precuneus-superior parietal lobule) and DMN (superior temporal gyrus and posterior cingulate cortex) provides additional arguments. This decreased connectivity suggests difficulties switching between externally oriented attention and internally oriented thoughts. Furthermore, studies have shown that decreased connectivity between the precuneus and temporal gyrus is correlated with the ADOS social score (84), highlighting the importance of these regions in ASD symptomatology.

Finally, the underconnectivity we observed between the right superior temporal gyrus in the DMN and the medial frontal gyrus in the SMN may modify the perception of somatosensory stimuli (100). Hence, an atypical representation of the body and its location in space provided by the SMN may participate in an atypical sense of self (101, 102). The decreased connectivity between the SMN and the DMN could participate in the atypical integration of external sensory information (SMN) and bind this external information to internal information (DMN).

Reduced connectivity between RSN may involve a blurring integration between external and internal information that would impact thought contents at rest (103). This difficulty integrating sensory details to a self-information, with a more global level, could be linked to the theory of the weak central coherence (104). The sensory over-responsivity may lead to a chronic state of hyperarousal at rest (105) that impacts the internal organization. Beyond, these results of atypical communication between external and internal information may be related to a weakened embodied cognition. Embodied cognition refers to the theory that the environment and the body (e.g., sensorimotor information) influence cognition (e.g., memory, mental simulation, attention, etc.) with a bottom-up process and, reversely, how cognition influences the body through a top-down process. Some studies report a decrease or lack of embodiment effect in ASD with poor integration of sensorimotor information (106–108). Reduced functional connectivity may modify the conscious perception of the external world in relation to self-referential processes, leading to social difficulties (31, 109–111).

The present study also highlighted three key nodes, which presented decreased current source density. First, the medial frontal gyrus was associated with reduced connectivity within and between RSNs, suggesting atypical multisensory integration, feeling of body ownership (112), and imitation skills, which play a role in social skills (95, 96). Second, the precuneus is involved in spatial functions of self and the spatial environment and may contribute to the altered sense of self and agency in ASD (101, 102). The precuneus, with a more lateral cortex, is also involved in attentional shifting from low- (e.g., simple stimulus) to high-level processing (shifting mental representations; e.g., theory of mind; 113,114). The precuneus associated with the posterior cingulate cortex contributes to self/other referential processing (115, 116) and the monitoring of the environment (117). Third, the posterior cingulate cortex’s underconnectivity with the superior temporal gyrus and precuneus may account for difficulty regulating and balancing the focus of attention on internal or external thoughts (118), arousal, and awareness (119), self-referential thought (120), and affect the stability of the brain network over time (whole-brain metastability; 121). Given their involvement, abnormalities in these brain regions may significantly impact social disabilities in autism.

The study had three main limitations. First, although EEG directly measures cerebral activity and identifies some brain regions using sLORETA, this technique has spatial limitations. As such, other methods would be useful for determining spatial topographies. Second, we focused on adolescence as defined by Sawyer et al. (122) (participants aged 1025 years in the present study), but it would be relevant to go further by examining neurodevelopmental changes during this period. Finally, our population was composed of high-functioning participants, limiting generalization to adolescents across the spectrum.

We report reduced functional connectivity within and between DMN, SMN, and DAN in the ASD group relative to the TD group. We hypothesize that the ASD group was less effective in modulating communication between brain regions in the alpha band and may lead to altered integration and switching between externally oriented information and internally oriented thoughts that may account for a “disembodied” cognition. Atypical alpha synchronization at rest in ASD may be linked to neural excitation/inhibition imbalance, impacting integration difficulties that affect low and high cognitive processes. Indeed, connectivity analyses revealed atypicality in integrative brain regions that may account for difficulties in 1) perception (external): somatosensory integration of environment, 2) cognitive (internal) integration, and 3) reciprocal influence between the external environment and self-endogenous stimuli. These impairments in the embodiment may contribute to social abilities: high-level integration required for related self-processing, notably interaction (e.g., nonverbal communication such as gestures, facial expressions, and modulation of timing and intonation of speech) and theory of mind, which requires individuals to switch from a self-perspective to another perspective. Further studies could be relevant to test the direct impact of these atypical brain functioning on embodied processes in ASD.

ADOS: Autism Diagnostic Observation Schedule

AQ: Autism spectrum Quotient

ASD: Autism spectrum disorder

DAN: dorsal attention network

DMN: default mode network

DSM: Diagnostic and Statistical Manual of Mental Disorders

EEG: electroencephalography

fMRI: functional magnetic resonance imaging

MEG: magnetoencephalography

MNI: Montreal Neurological Institute

ROI: regions of interest

RS: resting-state

RSN: resting-state network

sLORETA: standardized Low-Resolution Brain Electromagnetic Tomography

SMN: sensorimotor network

SnPM: statistical non-parametric mapping

TD: typically developing

8.1 Ethics approval and consent to participate

The studies involving human participants were reviewed and approved by the French ethic committee (CPP Nord Ouest III, N◦ 2014-02). Written informed consent to participate in this study was provided by the participant's legal guardian/next of kin for minor participants and directly by the participant for major participants.

8.2 Consent for publication

Not applicable

8.3 Availability of data and materials

The datasets used and/or analyzed in the current study are available from the corresponding author upon reasonable request.

8.4 Competing Interest

The authors declare that they have no competing interests.

8.5 Funding

This study was supported by the French Clinical Research Hospital Program (2014-A00481-46).

8.6 Authors' contributions

PW collected all the data and participated in the analysis and writing. PC participated in the collection of the data, methods, and analysis. FD collected the data, directed the EEG recording, and made it possible to collect the EEG data. FW, PD, CM, JMG, and FG worked on including patients and their clinical exploration. MM and FW participated in the methods and relecture. BGG designed the study participated in writing. PC, FD, FW, MM, PD, CM, JMG, FG, FE, and JMB provided substantial modifications to the manuscript. All authors read and approved the final manuscript.

8.7 Acknowledgments

The authors would like to thank the autism resource centers in Caen and Amiens, the Amiens Hospital team, and more particularly Camille Rébillard, Clémentine Piet, Lucie Raoul, Thomas Hinault, Aurore Malpart, Isabelle Popov, and Pauline Brunel. We are grateful to Elizabeth Wiles-Portier for reviewing the English style. We are also indebted to all the participants and their families who took part in this study.

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Additionalfile1.xls
Title of data: Significate difference in current source density in alpha band for ASD and TD groups (p < 0.05). Description of data: For each region that showed significant decrease in current source density, the number of voxels, the localization of the maximum statistical peak difference, and the involvement in a RSN of interest for this region were indicated. The names of the regions are the labels given by sLORETA for the corresponding coordinates.

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You are reading this latest preprint version

EEG resting-state functional connectivity: evidence for an imbalance of external/ internal information integration in Autism

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

1. Background

2. Methods

2.1. Participants

2.2. EEG recording and data acquisition

2.1. Source localization analysis and statistical non-parametric mapping (SNPM)

2.2. Functional Connectivity analysis

2.2.1. Regions of interest

2.2.2. Functional Connectivity Analyses

3. Results

3.1. Source Localization

3.2. Functional connectivity within RSNs

3.3. Functional connectivity between RSNs

4. Discussion

4.1. Alpha band: a relevant marker for studying ASD atypicality

4.2. Functional connectivity within resting-state networks

4.3. Functional connectivity between resting-state networks

5. Limitations

6. Conclusion

Abbreviations

Declarations

References

Supplementary Files

Status:

Version 1