Altered Intrinsic Brain Spontaneous Activities in Children With Autism Spectrum Disorder Comorbid ADHD

Abstract

Objective:

The study involved 17 children with Autism Spectrum Disorder (ASD), 21 with ADHD, 30 with both (ASD + ADHD), and 28 typically developing children (TD).

Methods:

The amplitude of low-frequency fluctuations (ALFF) was measured as a regional brain function index. Intrinsic functional connectivity (iFC) was also analyzed using the region of interest (ROI) identified in ALFF analysis. Statistical analysis was done via one-way ANCOVA, Gaussian random field (GRF) theory, and post-hoc pair-wise comparisons.

Results:

The ASD + ADHD group showed increased ALFF in the left middle frontal gyrus (MFG.L) compared to the TD group. In terms of global brain function, the ASD group displayed underconnectivity in specific regions compared to the ASD + ADHD and TD groups.

Conclusion:

The findings contribute to understanding the neural mechanisms underlying ASD + ADHD.

Keywords

autism spectrum disorder ADHD comorbidity amplitude of low-frequency fluctuations fnctional connectivity

Introduction

Autism spectrum disorder (ASD) and ADHD are common neurodevelopmental disorders that occur in childhood. The comorbidity rate of the two disorders is very high. It is reported that 30% to 80% of ASD patients are comorbid with ADHD, and 20% to 50% of ADHD patients also meet diagnostic criteria for ASD (Van et al., 2012). Studies have also shown that comorbid children have poorer adaptive functioning and more severe symptoms compared to those with ADHD or ASD only (Gadow et al., 2006; Mansour et al., 2017; Sikora et al., 2012). Furthermore, the comorbid condition can increase parenting stress and affect treatment efficacy (Hong et al., 2021). Moreover, there is evidence that the children with comorbid ASD and ADHD have lower response to and suffer from more side effects of standard ADHD medications in contrast with non-comorbid subjects (Reiersen & Todd, 2008). However, the etiology and pathological mechanism of the comorbidity of ASD and ADHD are not well understood

Up to now, there are very limited studies on the etiology and pathological mechanism of ASD comorbid with ADHD. Previous studies on the comparison between ASD and ADHD have shown that these two disorders overlap in etiology (Craig et al., 2015), specifically, they share similar familial/genetic factors (Ghezzo et al., 2009; Holtmann et al., 2007; Ronald et al., 2010; Taurines et al., 2012). In addition, the two disorders share common maternal risk factors such as psychotropic medication, pre-eclampsia, and infectious diseases (Cohen et al., 2011; Craig et al., 2015; Croen et al., 2011; Kroger et al., 2011; Lyall et al., 2012; Taurines et al., 2012). Moreover, there are also common neuroimaging underpinnings, including deactivation of the task-related default mode network (DMN) and differences in the gray matter (GM) volume in the left medial temporal lobe and the left inferior parietal cortex, between the two disorders (Brieber et al., 2007; Christakou et al., 2013). However, it is noteworthy that the deactivation of the DMN is not exclusive to ADHD and ASD. Similar deactivations have been observed in other conditions such as schizophrenia, Alzheimer’s disease, suggesting a broader neurobiological significance of DMN alterations across different neuropsychiatric disorders (Manoliu et al., 2014; Greicius et al., 2009).

Based on previous studies on the etiology and pathological mechanism of the comorbidity, several possible association models of ASD + ADHD, ASD, and ADHD have been proposed (Chabernaud et al., 2012). A genetic view holds that ASD + ADHD and non-comorbid conditions are different clinical manifestations sharing the same genetic basis (Moreno-De-Luca et al., 2013). A neuroimaging study on spontaneous brain activity revealed that ASD + ADHD is just a subtype of ASD (Itahashi et al., 2015). However, it was indicated in a study using task-based functional magnetic resonance imaging (MRI) that ASD + ADHD is a separate and relatively serious disorder (Chantiluke et al., 2014). Meanwhile, some researchers have conducted meta-analyses on functional and structural brain images and further proposed that ASD + ADHD is a transitional type of ASD. Specifically, pure ADHD with little or without impairment of social interaction is the mildest manifestation, ADHD with mild to severe impairment of social interaction is the moderate manifestation, and ASD is the severest manifestation (N. N. Rommelse et al., 2011; N. Rommelse et al., 2017).

Many of the above-mentioned hypotheses indicate that the pathological mechanism of ASD + ADHD is complicated and needs further exploration. Currently, neuroimaging is a non-invasive technology that has been widely used in studying the neural mechanisms of various mental disorders. To date, few neuroimaging studies have directly compared comorbid ASD and ADHD with the non-comorbid conditions, and largely inconsistent results have been obtained. For example, one resting-state functional MRI (rs-fMRI) study measuring the voxel-wise network centrality found that ADHD-specific differences in the basal ganglia and increased ASD-specific centrality in the temporolimbic areas are observed in both children with ASD + ADHD and non-comorbid children (Di Martino et al., 2013). Another study measuring functional brain network integrity did not detect common brain differences between comorbid and non-comorbid subjects (Dajani et al., 2019). Meanwhile, according to a task-based fMRI study, the comorbid group exhibited distinct and pronounced deviations in brain activation during temporal discounting tasks in comparison with the typically developing children (TD) group and the non-comorbid group (Chantiluke et al., 2014). However, it was demonstrated in a structural MRI study that there is no difference in the GM volume in the postcentral gyrus between the ASD + ADHD group and the TD group, while the ASD group displays an abnormal increase in the GM volume, and all children with ASD in the study (with or without ADHD) have increased surface area of the left inferior parietal cortex (Mahajan et al., 2016). Therefore, more neuroimaging studies, in which the ASD + ADHD group is included and directly compared with non-comorbid groups, are needed to explore the neural mechanism of ASD + ADHD and its relationships with ASD and ADHD.

Rs-fMRI is a powerful tool to assess spontaneous brain activity, which helps us to understand the neural mechanism under a taskless state (Biswal et al., 1995). Rs-fMRI is also suitable for research on children because the images can be collected quickly and the procedure requires minimal cooperation from the subject(Thomason et al., 2011). Hence, it is widely employed to study the neural mechanism of various mental disorders, including ASD and ADHD. Meanwhile, directly comparing the ASD + ADHD group with the ASD group and the ADHD group can better reflect the differences and connections among the resting-state brain function patterns.

In the realm of Rs-fMRI research, ALFF (Amplitude of Low Frequency Fluctuations) is a metric, quantifying the mean of amplitudes within a specific frequency domain—particularly, the 0.01 to 0.1 Hz range employed in our investigation. The robust nature of ALFF makes it an practical metric for fMRI researchers due to its ease of collection and suitability for inter-study comparisons (Wang et al., 2018; Zang et al., 2007). On the other hand, intrinsic functional connectivity (iFC) provides insight into the synchrony of these spontaneous fluctuations between different brain regions, reflecting the underlying functional networks and interactions. By combining these two measures, a comprehensive picture of both regional and global brain neural activities can be captured. There have been no studies on the amplitude of low-frequency fluctuations (ALFF; Wang et al., 2018; H. Yang et al., 2011; Zang et al., 2007) and the resting-state intrinsic functional connectivity (iFC; Biswal et al., 1995) of ASD + ADHD yet, but previous studies have manifested that the ALFF and iFC variations occur in both ASD (Guo et al., 2017; Itahashi et al., 2015) and ADHD (Zang et al., 2007). Given the above findings, we recruited drug-naïve ASD + ADHD, ASD, ADHD and TD subjects, aiming to characterize the patterns of regional (ALFF) and global (iFC) brain functions of children with ASD + ADHD, and directly compared it with those of TD, ASD, and ADHD groups. We hypotheses that the ASD + ADHD group has distinct regional and global rs-fMRI patterns compared with those of the other three groups.

Methods

Subjects

We chose children with ASD, ADHD, and ASD + ADHD who were recruited from the outpatient clinic of Peking University Sixth Hospital from June 2017 to September 2019 from ongoing studies, and enrolled TD through posters and online advertisements from the surrounding communities. The diagnosis of ASD, ADHD, and ASD + ADHD was clinically confirmed by two experienced child and adolescent psychiatrists based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5; Arbanas, 2015). All subjects were Han Chinese and aged 6 to 18 years old, with right-handedness and had an IQ of at least 70 (measured by the Wechsler Intelligence Scale for Children-Chinese Revised (WISC–CR), Third or Fourth Edition; Gong & Cai, 1994; H. Zhang, 2009). Subjects with major physical or neurological illness, or psychiatric conditions other than ASD and ADHD were excluded based on the Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL; Kaufman et al., 1997). The subjects with contraindications for MRI scanning or a history of psychiatric or neurological drug use were excluded through clinical reviews. The study was approved by the Ethics Committee of Peking University Sixth Hospital. Written informed consent was obtained from both the subjects (if capable to sign) and their parents/legal guardians for subjects.

A total of 101 children aged 6 to 18 years old [mean ± standard deviation (SD)] from ongoing studies were included in this study, and 5 children (2 with ASD, 1 with ADHD and 2 with ASD + ADHD) were excluded due to excessive movement [mean framewise displacement (FD): 0.272 mm (2 SD of the mean in the initial subject group, 0.108 mm + 2 × 0.082 mm; Yan et al., 2013). Finally, a total of 17 children with ASD, 21 children with ADHD, 30 children with ASD + ADHD, and 28 TD who were matched for gender, age, and head motion were included in this study (see Table 1 for detailed demographic information).

Table 1.

Characteristics of the Four Groups.

	ASD (n = 17)	ADHD (n = 21)	ASD + ADHD (n = 30)	TD (n = 28)	χ²/F	p	Post-hoc
Gender (male:female)	17:0	19:2	27:3	24:4	2.568	.463	—
Age (median)	12	11.5	10.6	12.3	7.682	.053	—
Full-scale IQ (M ± SD)	107.88 ± 23.84	103.86 ± 13.58	100.26 ± 17.96	113 ± 12.43	9.092	.028*	ASD = ADHD = TD ASD + ADHD < TD
Head motion (FD) (M ± SD)	0.116^†	0.072^†	0.100^†	0.075^†	7.013	.071	—

Note. ASD = autism spectrum disorder; Comorbidity = ASD comorbid with ADHD; TD = typically developing children; FD = framewise displacement; IQ = intelligence quotient; M = group mean; SD = standard deviation of the group mean.

The superscript “^†” indicated the median, and non-parametric test was performed for comparison between groups, and “*” indicates that the p value is less than .05.

MRI Data Acquisition

A GE Discovery MR750 3.0T MRI scanner at the Imaging Center of Peking University Sixth Hospital was employed, and standard head coils were used for radio-frequency (RF) transmission and MRI signal reception. Before scanning, subjects were familiarized with the environment of the scanning room and the scanning process. During scanning, the subjects were required to lie flat on the scanning bed with eyes closed and were instructed not to move or think systematically.

All of the subjects completed an 8-minute resting-state scan using a multi-echo echo-planar imaging (EPI) sequence [repetition time (TR) = 2000 ms, echo time (TE) = 30 ms, flip angle = 90°, 43 slices, matrix = 64 × 64 and field of view (FOV) = 220 × 220 mm], and T1-weigthed anatomical images [TR = 4.78 ms, TE = 2.024 ms, inversion time (TI) = 400 ms, flip angle = 15°, 192 slices, FOV = 240 mm × 240 mm, and voxel size = 0.94 mm × 0.94mm × 1 mm] comprised of 192 contiguous volumes were acquired.

Preprocessing

Data Processing Assistant for Resting-State fMRI (DPARSF; Chao-Gan & Yu-Feng, 2010; http://rfmri.org/DPARSF), which was developed based on Statistical Parametric Mapping Version 12 (SPM12) package (http://www.fil.ion.ucl.ac.uk/spm) and Data Processing & Analysis for Brain Imaging (DPABI) toolkit (http://rfmri.org/DPABI; Yan et al., 2016), was applied to preprocess the images as follows: (1) The first 10 volumes were discarded to obtain steady MRI signals. (2) The functional images were corrected for the acquisition time delay between slices of each volume. (3) Six-parameter (rigid-body) linear transformation was used to realign the head motion between volumes. (4) The motion was corrected by 6 degrees-of-freedom linear transformation, and the T1-weighted images were co-registered to the mean functional images. (5) The transformed T1-weighted images were segmented into GM, white matter (WM) and cerebrospinal fluid (CSF) by virtue of unified segmentation algorithm according to the prior tissue maps of SPM (Ashburner & Friston, 2005). (6) To reduce the effect of head motion, a Friston 24-parameter model (Friston et al., 1996) was utilized to regress out this covariant, while the WM signal and CSF signal were regressed out from the time course of each voxel. (7) Each native space was transformed to a Montreal Neurological Institute (MNI) space based on the Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra (DARTEL) (Ashburner, 2007). (8) The images were smoothed by Gaussian smoothing kernel with a full width at half-maximum (4 mm). (9) All images were filtered by temporal band-pass filtering (0.01–0.1 Hz) to reduce low-frequency drift and high-frequency physiological noise.

Statistical Analysis

The ALFF (0.01–0.1 Hz) of all subjects were calculated using DPARSF, and then converted to Z-score based on the mean ± SD across all voxels. One-way analysis of covariance (ANCOVA), with the age, full-scale IQ, gender, and mean FD as the covariates and with the group as a four-level factor, was conducted to test the diagnostic group effects of ALFF, and F-contrast was applied to measure the group effects. Gaussian random field (GRF) theory was used for multiple comparisons (voxel-level threshold was set as p < .001, and cluster significance was set as p < .05). The brain regions with differences in ALFF values among the four groups were taken as the regions of interest (ROIs) to calculate the whole-brain iFC as follows: (1) The representative mean time series for each region was extracted by averaging the time series of all voxels therein. (2) The correlation coefficient between the ROI and the mean time series of other voxels of the whole brain was calculated, and then Fisher’s r to z transformation was performed on the correlation coefficient for all ROI and iFC pairs. Furthermore, one-way ANCOVA was carried out to test the diagnostic group effects on functional connectivity, with the age, full-scale IQ, gender, and mean FD as the covariates. The GRF theory was employed to correct for multiple comparisons (voxel-level threshold was set as p < .001, and cluster significance was set as p < .05). For the iFC with differences among groups, Bonferroni correction (corrected p < .0012) was adopted to for multiple comparisons. Finally, the significant clusters identified in previous steps via Tukey’s post-hoc test were used to determine the pair-wise differences between groups.

Results

ALFF Analysis

One brain region with a significant difference among the four groups centered on the left middle frontal gyrus (MFG.L) and extended to the Brodmann area 10 (BA10) was identified by means of one-way ANCOVA (Figure 1). The Tukey’s post-hoc test results indicated that the ALFF of this region was significantly decreased in the ASD + ADHD group compared with that in the TD group (p = .037), while no significant differences were detected among the remaining groups (Figure 2, Table 2). In addition, both the ASD group and the ASD + ADHD group showed positive ALFF values of MFG.L while the ADHD group and TD group showed the opposite. Obviously, the ALFF value of MFG.L of ASD + ADHD group was more similar to those of the ASD group than the ADHD group and the TD group.

Figure 1.

ALFF with differences among the four groups.

Figure 2.

ALFF and iFC with differences among the four groups.

Table 2.

One-way ANCOVA and F-contrast of ALFF Among the Four Groups [z score (M ± SD)].

Cluster region	Cluster size #voxels	Peak MNI (mm)			ASD	ADHD	ASD + ADHD	TD	ANCOVA p-value	Cohen’s f
Cluster region	Cluster size #voxels	X	Y	Z	ASD	ADHD	ASD + ADHD	TD	ANCOVA p-value	Cohen’s f
MFG.L	30	−21	39	27	0.010 ± 0.22	−0.121 ± 0.21	0.030 ± 0.30	−0.151 ± 0.16	.000	0.597

Note. DPABI (http://rfmri.org/dpabi) and CUI Xu’s xjView (http://www.alivelearn.net/xjview/) were used to calculate cluster peaks. Cluster size was presented as the number of voxels (2 mm × 2 mm × 2 mm). Gaussian random field theory was employed to correct for multiple comparisons (voxel-level threshold was set as p < .001, and cluster significance was set as p < .05). MFG.L = left middle frontal gyrus.

Resting-State iFC Analysis

The brain region (MFG.L) identified in the ALFF analysis was regarded as the ROI to calculate the whole-brain iFC in the four groups separately, followed by one-way ANCOVA among the four groups. Through multiple comparisons using GRF theory and pair-wise comparisons via Tukey’s post-hoc test, it was found that (a) similar iFC patterns were observed among the ASD + ADHD group, the ADHD group, and the TD group. (b) Compared with the ASD + ADHD group and the TD group, the ASD group exhibited decreased connectivity between the MFG.L and other four brain regions [namely, the left calcarine fissure (Cal.L), the left middle occipital gyrus (MOG.L), the right middle temporal gyrus (MTG.R), and the left precuneus (PreCu.L)]. (c) The connectivity between the MFG.L and the MOG.L was decreased in the ASD group in comparison with that in the ADHD group, but the difference was no longer statistically significant after Bonferroni correction (Tables 3 and 4 and Figure 2). To sum up, the iFC of MFG.L of the ASD + ADHD group was more similar to those of the ADHD group and the TD group, and stronger than that of the ASD group.

Table 3.

One-way ANCOVA and F-contrast of Functional Connectivity [z score (M ± SD)].

Functional connectivity	Cluster size #voxels	Peak MNI (mm)			Peak intensity	ASD	ADHD	ASD + ADHD	TD	ANCOVA p-value	Cohen’s f
Functional connectivity	Cluster size #voxels	X	Y	Z	Peak intensity	ASD	ADHD	ASD + ADHD	TD	ANCOVA p-value	Cohen’s f
MFG.L—Cal.L	50	18	−57	15	9.01	0.007 ± 0.10	0.213 ± 0.12	0.219 ± 0.13	0.252 ± 0.13	.000	0.665
MFG.L—MOG.L	60	−36	−78	21	10.36	−0.016 ± 0.11	0.123 ± 0.17	0.191 ± 0.16	0.198 ± 0.14	.000	0.592
MFG.L—MTG.R	178	21	−48	18	15.02	−0.042 ± 0.15	0.111 ± 0.11	0.166 ± 0.13	0.156 ± 0.09	.000	0.719
MFG.L—PreCu.L	100	−6	−75	36	11.24	0.018 ± 0.14	0.168 ± .0.14	0.204 ± 0.14	0.226 ± 0.15	.000	0.597

Table 4.

Tukey’s Post-hoc Test on Functional Connectivity (p value).

Functional connectivity	ASD × ADHD	ASD × (ASD + ADHD)	ASD × TD	ADHD × (ASD + ADHD)	ADHD × TD	(ASD + ADHD) × TD	Peak intensity
MFG.L—Cal.L	0.102	0.011*	0.007**	0.747	0.711	0.823	9.01
MFG.L—MOG.L	0.038*	0.011*	0.0057**	0.850	0.734	0.673	10.36
MFG.L—MTG.R	0.157	0.006**	0.004**	0.595	0.550	0.955	15.02
MFG.L—PreCu.L	0.117	0.007**	0.009**	0.725	0.760	0.857	11.24

Note. p value after GRF correction is displayed. R stands for right and L stands for left.

Indicates that the p value is less than .05 after GRF correction,

Indicates that the p value is less than .01 after GRF correction.

Discussion

In this study, regional and global resting state functional connectivity was compared among TD, ASD, ADHD, and ASD/ADHD comorbid individuals using ALFF and iFC. The results showed that there were ALFF variations in the MFG.L, but there were no such variations in the iFC between the MFG.L and other brain regions in the ASD + ADHD group, and such patterns were different from those of the ASD group and ADHD group. To our knowledge, this is the first rs-fMRI study to compare the resting-state brain function patterns of subjects with comorbid ASD and ADHD with non-comorbid individuals.

In terms of the resting-state regional brain function, it was discovered that the ALFF values in the MTG.L extended to the BA10 of the ASD + ADHD group was higher than those of the TD group, while the ASD group and the ADHD group did not exhibit significant differences compared to the TD group, indicating that the ASD + ADHD group may have different resting-state regional brain function patterns compared with the ASD group and the ADHD group. The above-mentioned results can be explained from various perspectives. For example, Moreno-De-Luca et al. (2013) proposed that the etiology of both ASD and ADHD can cause variations in the MFG.L, but these variations are not severe enough to be identified by rs-fMRI. However, ASD + ADHD may have a certain synergistic effect on such etiology to increase the severity of the variations in the MFG.L. At present, there is no research on the comparison of the resting-state regional brain function of subjects with ASD + ADHD for our reference, but there are many studies on the regional brain function of the ASD and ADHD groups. Prior evidence has emphasized atypical regional brain function in the left middle frontal gyrus (MFG.L) in both the ADHD and ASD groups (Alaerts et al., 2015; Hwang et al., 2019; Jiang et al., 2015; X. R. Yang et al., 2015), suggesting that this brain region is related to both ASD and ADHD. Therefore, the differences in the resting-state regional brain function of this brain region in the ASD + ADHD group may be attributed to the superposition of common variations in the ASD group and the ADHD group. However, no differences in the resting-state regional brain function between the ASD group and the TD group, nor between the ADHD group and TD group in this study. This result is inconsistent with the findings in most previous studies, which is probably associated with the small sample size of this study. Other possibilities should also be considered in addition to the explanation of superposition effects. For instance, ASD + ADHD as a unique type of disorder may be another explanation (Chantiluke et al., 2014; Sinzig et al., 2009). If so, the MFG.L is probably one of the disorder-specific biomarkers of ASD + ADHD, but this hypothesis needs to be further explored and verified.

Whether the variations only occur in the MFG of the ASD + ADHD group needs to be further explored and discussed. Nevertheless, according to previous studies on the function of the MFG and the correlation of the MFG variations with the symptoms of ASD and ADHD, the MFG variations may be related to the clinical symptoms of the subjects with ASD + ADHD. The MFG, or a larger region known as “dorsolateral prefrontal cortex (DLPFC)” that contains this gyrus, is an important brain region involved in multiple brain networks such as DMN, face-processing network and executive control network (Di Martino et al., 2009; Seeley et al., 2007), and it plays a crucial role in higher-order cognitive functions including executive function and face recognition (Geurts et al., 2013; Guo et al., 2017; Talati & Hirsch, 2005). It has been reported that the atypical functions of the MFG are not only related to the dysfunction in processing disgusted and happy faces, but are also associated with the dysfunction in facial recognition and social interaction in children with ASD (Daly et al., 2012; Geurts et al., 2013). Furthermore, research has demonstrated that functional differences in the bilateral middle frontal gyrus (MFG) are associated with variations in cognitive functions, such as spatial working memory and inhibitory control, in individuals with ADHD (Tafazoli et al., 2013). Additionally, the BA10 and adjacent prefrontal regions exert high-order executive control, and they are considered to be able to mediate the executive and attentional processes, and participate in the pathophysiology of ADHD (Solanto et al., 2009). In summary, the variations in the MFG may be associated with the clinical symptoms of both ASD and ADHD.

The MFG.L exhibiting atypical regional brain function was selected as the ROI, and the whole-brain iFC was compared among the four groups. The results showed that there was no significant difference between the ASD + ADHD group and the TD group. Since there are no other studies on the comparison of the whole-brain iFC between the ASD + ADHD group and the TD group for our reference, and the sample size of this study was relatively small, whether the results can objectively reflect the iFC of the MFG in the subjects with ASD + ADHD remains unclear and needs to be further verified by increasing the sample size.

In this study, with the MFG.L as the ROI, no significant differences in the whole-brain iFC were observed between the ADHD group and the TD group. The negative results obtained from the ADHD group are consistent with the findings in several previous studies (Elton et al., 2014; Zhou et al., 2019), but many studies have shown that the subjects with ADHD have atypical iFC of multiple brain regions (Fair et al., 2010; Konrad & Eickhoff, 2010), such as reduced DMN coherence (Fair et al., 2010) and increased temporal coherence between the left DLPFC and several brain regions in the DMN (Hoekzema et al., 2014). This may be related to the differences such as age, gender, IQ in the samples of these studies.

It was also uncovered in this study that compared with the TD group, the ASD group showed decreased iFC between the MFG.L and other four brain regions [the Cal.L, the MOG.L (extended to superior occipital gyrus, fusiform gyrus and parahippocampal gyrus), MTG.R, and PreCu.L], which is consistent with the results in previous research. Many previous rs-fMRI studies on ASD have confirmed that the iFC of the MFG of subjects with ASD is atypical (Nielsen et al., 2013). For example, a study on the iFC of the DMN revealed that there is dysconnectivity between the MFG.L and the MOG.L in the ASD group compared with that in the normal control, and the affected brain regions encompass the parahippocampal gyrus, the fusiform gyrus, and the insula (Nielsen et al., 2013). Additionally, the ASD-related dysfunctions found in this study are reported to be associated with clinical symptoms. For example, the degree of reduced iFC between other DMN regions, such as the precuneus and the medial prefrontal cortex, are associated with the severity of social communication defects in the ASD group (Assaf et al., 2010). Furthermore, the iFC between the frontal gyrus and the precuneus is probably related to the lack of ASD subjects’ ability to respond to social cues (such as social information processing and facial emotion processing) (Guo et al., 2017; S. Zhang & Li, 2012). In this study, the ASD group had the above-mentioned atypical patterns compared with the ASD + ADHD group, suggesting that the whole-brain iFC patterns of the MFG.L in the ASD + ADHD group are different from those in the ASD group.

There are many possible reasons for similar iFC patterns in the ASD + ADHD group to those in the TD group and the ADHD group but different iFC patterns from those in the ASD group when MFG.L is used as the ROI. It was found that the ASD group had decreased iFC of the MFG, while the ADHD group showed the opposite trend, which is consistent with the tendencies in some previous studies (Assaf et al., 2010; Di Martino et al., 2013; Jung et al., 2019; Nielsen et al., 2013). For example, it is reported that the ASD subjects manifest decreased connectivity between nodes of the brain’s DMN including the MFG (Cherkassky et al., 2006; Wiggins et al., 2011), while the ADHD subjects have increased iFC between the right prefrontal region and the left cuneus (Wolf et al., 2009), as well as more significant iFC between the dACC and the thalamus, cerebellum, insula, and pons (Tian et al., 2006). Based on the above findings, the similarity between the ASD + ADHD group and the TD group could be attributed to the co-occurrence of conflicting tendencies of ADHD and ASD in the same individual, which causes an offset effect. In this regard, it has been proposed in some literature that the subjects with ASD + ADHD have a kind of “protective” effect which helps compensate the differences in the regional spontaneous brain activity (Mahajan et al., 2016). The conjecture of offset effect or compensation effect may provide some pathological explanations for the model proposed by N. N. Rommelse et al. (2011) and N. Rommelse et al. (2017): Both ASD and ADHD actually compose a continuous spectrum. Specifically, pure ADHD with little or no impairment of social interaction is the mildest manifestation, ADHD with mild to severe impairment of social interaction is the moderate manifestation, and ASD is the severest manifestation (N. N. Rommelse et al., 2011; N. Rommelse et al., 2017). In the present study, the ADHD group exhibited no differences in the regional and global brain functions compared with the TD group. The ADHD + ASD group showed variations in the regional brain function while maintaining typical global brain function. The ASD group exhibited typical regional brain function while showing widespread differences in global brain function across multiple brain regions. These results may provide some evidence for the model established by Rommelse. However, it is known that the above-mentioned changes in brain functions and clinical symptoms are not absolutely parallel (Desmond & Chen, 2002). Besides, since there is scarce evidence on the differences in brain functions of subjects with ASD + ADHD, the results and corresponding explanations should be treated with caution, and further research is needed to verify such results and explore the clinical implications. Finally, assuming that ASD + ADHD are simple superposition of ASD and ADHD or a subtype of ASD, the subjects with ASD + ADHD should at least have similar patterns to those with ASD. Obviously, the existing results are not sufficient enough to support these two hypotheses.

There were several limitations in this study. First of all, the sample size of this study was relatively small, which may limit our ability to detect statistically significant differences between groups. Secondly, given the possible changes in the structural and functional brain activities of the ASD and ADHD subjects with the age indicated in previous studies (Alaerts et al., 2015; Choi et al., 2013), the wide age distribution [from childhood to adolescence (6–18 years old)] of the subjects in this study could lead to conflicting findings of functional connectivity (Uddin et al., 2013). The underrepresentation of female participants in our study is also a limitation. Given known gender differences in brain structure and function, this imbalance might impact the generalizability of our findings to the broader female ASD, ADHD, and ASD + ADHD populations. Thirdly, it is difficult to generalize the results to all individuals with ASD, ADHD, and ASD + ADHD because of the inclusion criteria that demanded a relatively high IQ and small head motion for better cooperation in the MRI scanning. Fourthly, considering that the micro-movement in the patient groups was greater than that in the TD group, although the micro-movement used as a group-level covariate in analyses was regressed out by “scrubbing,” the influence of the micro-movement on the diagnosis-related differences cannot be excluded completely. Fifthly, the group-related differences in the ALFF were undetectable during global signal regression (GSR) that was used to account for nuisance signals such as GM signals, scanner-related noises, and excessive motions. Theoretically, GSR can increase the sensitivity (Murphy et al., 2009), but whether it is applicable to seed-based connectivity studies is still unknown (Carbonell et al., 2011; Fox et al., 2009; Murphy et al., 2009; Satterthwaite et al., 2013). Finally, the clinical implications of the aberrant brain regions were not explored in this study, which remains a research direction in the future.

Conclusion

In conclusion, the co-occurrence of ASD and ADHD is a common clinical phenomenon, but the pathological mechanism of it was not well understood. This study explored the resting-state brain function patterns of subjects with comorbid ASD and ADHD, and found that compared with the TD group, the ASD + ADHD group exhibited variations in the resting-state regional but not global brain function patterns, and these patterns were different from those of the ASD or ADHD group. These results indicate that ASD + ADHD are neither simple superposition of ASD and ADHD nor a subtype of ASD. Moreover, this study provides primary understanding for the neural mechanism of ASD + ADHD. However, it is necessary to expand the sample size and conduct research based on multimodal MRI for further understanding the neural mechanism of ASD + ADHD.

Footnotes

Acknowledgements

This paper is dedicated to Joe Biederman. In March 2005, at the “ADHD International Workshop on Attention Deficit Hyperactivity Disorder” organized by Professor Wang Yufeng, Joe Biederman gave a report on comorbidities of ADHD. This report has great inspiration for conducting comorbidity research on ADHD in China. Since then, we have conducted a series of comorbidity studies on ADHD, from genetic to clinical research. We believe Joe Biederman will be pleased to see more meaningful results from our research in this area.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the National Key R&D Program of China (Grant Number: 2017YFC1309900), the National Natural Science Foundation of China (Grant Number: 81873804, 81471382), the Beijing Municipal Science, and Technology Commission (Grant Number: Z181100001518005, 7164314). We are grateful to the children and their parents for involvement in our study.

Ethical Approval

The authors complied with APA ethical standards in the treatment of subjects and this work was approved by the Ethics Committee of Peking University Sixth Hospital.

ORCID iD

Xinzhou Tang

Author Biographies

Xinzhou Tang is a clinical Ph.D. graduate from Peking University Health Science Center, specializing in child psychiatry and mental health. She has authored an article as the first author in SCI. Currently, she holds a post at Beijing Children’s Hospital, working in the field of child psychiatry research.

Zenghui Ma received a master’s degree in Psychiatry and Mental Health from Peking University Sixth Hospital and a bachelor’s degree in Clinical Medicine from Sun Yet Sen University. Her current field placement is with telehealth tools for identification of autism spectrum disorder. She is interested in autism screening and diagnostics.

Kat SiuChing is an attending physician at the Department of Pediatrics, Peking University Sixth Hospital. He received a bachelor’s degree in clinical medicine from Peking University Health Science Center and a doctor’s degree in medicine from the Institute of Mental Health of Peking University. He is interested in child and adolescent psychiatry, psychometrics and social psychology.

Lingzi Xu recieved her masters degree in epidemiology and biostatistics from John’s Hopkins University and her medical degree from Peking University. She completed psychiatric residency training at Peking University Institute of Mental Health. Her current area of interest is mechanisms and novel treatment methods for child and adolescent mental disorders.

Qinyi Liu is a second-year doctoral student at the Peking University Institute of Mental Health in Beijing, China. She earned her Bachelor’s degree in Clinical Medicine from Peking University. Her current research concentrates on the screening and diagnosis of autism spectrum disorders, with a special interest in eye-tracking and neuroimaging studies related to autism.

Li Yang is a child psychiatrist and professor at National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital. She is Vice President of World Federation of ADHD, deputy director of Chinese Society of Child and Adolescent Psychiatry, and deputy director of Child Mental Health Centre of Peking University Sixth Hospital. Her research interests focus on child and adolescent psychiatric disorders, especially ADHD and ASD, using dimensional evaluation of psychopathological traits to discover and validate new genes regulating neurodevelopment.

Yufeng Wang is a child psychiatrist and professor at National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital. She is an awardee of WFADHD medal. She dedicated herself to the communication and collaboration on ADHD between eastern and western countries. She has trained about 100 PH.D.s and M.D.s in China, and contribute greatly to the development of child and adolescent psychiatry in China.

Qingjiu Cao is a child psychiatrist and Chief Physician, currently serving as the Director of the Children’s Ward at Peking University Sixth Hospital. He earned his Ph.D. in Psychiatry and Mental Health from Peking University in 2007. He has published dozens of articles in SCI-indexed journals, primarily focusing on the neuroimaging and neuropsychology of neurodevelopmental disorders. Professor Cao has spearheaded numerous key research projects funded by the National Natural Science Foundation and the National Key R&D Program, predominantly investigating autism spectrum disorders, ADHD, and their associated neuropathological defects. Additionally, he has held pivotal roles in several academic conferences and professional committees.

Xue Li is a child psychiatrist and associate professor of Psychiatry at National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital). Her academic interest is research in psychiatric and developmental disorders, especially Autism Spectrum Disorder (ASD). She has published dozens of papers and books and made various contributions to the clinical and research fields of ASD in China.

Jing Liu is a child psychiatrist and Professor of Psychiatry at National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital). She is also the director of the hospital’s Centre for Child Mental Health. She holds pivotal roles in international or national academic professional committees. She is an awardee of IACAPAP medal. She has done a lot of research in the field of child and adolescent mental disorders and neurodevelopmental disorders, and has published more than one hundred papers related with neurodevelopmental disorders, and is the co-editor in chief of the second version of Chinese ADHD Prevention and Treatment Guideline. All of which promote the development of the field of child mental health in China.

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