Delineation of Functional Changes and Associated Cortical Transcriptomic Profiles for Autism Spectrum Disorders

doi:10.21203/rs.3.rs-2610086/v1

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Delineation of Functional Changes and Associated Cortical Transcriptomic Profiles for Autism Spectrum Disorders

https://doi.org/10.21203/rs.3.rs-2610086/v1

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Background: Although abnormal functional activities or connectivities in individuals with autism spectrum disorders (ASD) have been reported, yet, there is still lack of consistent findings of altered functional activities or connectivities and its associated genetic basis for ASD.

Methods: Here, we aimed to identify consistent brain functional alterations and the biological genetic mechanisms in children and adults with ASD using a large sample of resting-state functional magnetic resonance imaging data from the Autism Brain Image Data Exchange dataset (ABIDE). Multi-site based meta-analyses were first employed and identified abnormal brain activity, local and global functional connectivities primarily localizing in frontal-temporal-occipital regions and default mode network (DMN) in individuals with ASD.

Results: The significant correlations between the aberrant brain regions and clinical phenotypes related to social functions in ASD were found. Importantly, brain-wide transcriptome-neuroimaging spatial association analyses and the follow-up enrichment, tissue-specific expression, and protein-protein interaction analyses were conducted and identified the underlying cellular and molecular mechanisms of ASD.

Conclusions: Collectively, our findings link molecular and functional changes in ASD and provide new evidence for neuropathology and a potential precious treatment target for ASD.

Autism spectrum disorder

resting-state fMRI

meta-analysis

transcriptome

Autism spectrum disorder (ASD) is one of the most common neurodevelopmental disorders caused by both genetic and environmental risk factors (Abrahams and Geschwind, 2008). The typical characteristics of ASD are impaired social communication and interaction, restricted interests, and repetitive behaviors (Lombardo et al., 2007). Currently, the clinical diagnosis of ASD is primarily through questionnaires and clinical observations which are prone to be subjective and susceptible (Baio et al., 2018; Wingate et al., 2014). Magnetic resonance imaging (MRI) is a non-invasive technique to detect brain structure and function and opens a new avenue to establish objective and reliable neuroimaging biomarkers for ASD (Di Martino et al., 2014; Smith et al., 2004). Due to small samples and high heterogeneity, there is still lack of consistent and reproducible neuroimaging biomarkers for ASD.

Resting-state functional MRI (rs-fMRI) is developed to evaluate brain intrinsic spontaneous fluctuations by measuring blood-oxygen-level dependent (BOLD) signals (Fox and Raichle, 2007; Raichle, 2010). To date, there are growing and expanding approaches or techniques developed to parse fMRI data, of which amplitude of low-frequency fluctuation (ALFF), local regional homogeneity (ReHo) and degree centrality (DC) are three widely used and acceptable measurements to characterize brain functional activity, local and global functional connectivity strength, respectively (B Biswal 1995; Cheng et al., 2022a; Cheng et al., 2022b; Zang et al., 2004; Zuo et al., 2012). Although a large number of literature have reported abnormal ALFF, ReHo, or DC in ASD, these cross-sectional results are problematized by inconsistent findings since small samples and patients’ homogeneity (Di Martino et al., 2013; Guo et al., 2017; Paakki et al., 2010). Importantly, ASD has been demonstrated to be a highly heritable disorder in which genetic variations play a crucial role in brain functional impairments (Erraji-Benchekroun et al., 2005; Pletikos et al., 2014). Thus, linking gene expression measured using transcriptome to brain activity, local and global functional connectivity changes could facilitate elucidating gene regulatory mechanisms for ASD (Willsey et al., 2013; Xie et al., 2021).

In the present study, we aimed to identify consistent changes in functional activity, local and global functional connectivity in ASD using multi-site meta-analysis and to link these neurophenotype changes with gene expression to reveal the genetic basis for ASD. First, we identified consistent alterations in brain functional activity, local and global functional connectivity separately measured with ALFF, ReHo, and DC using meta-analysis of all the statistical maps between ASD and healthy controls (HC) from different sites. Then, brain-wide transcriptome-neuroimaging spatial association analyses were conducted to identify the associated genes with differences in ALFF, ReHo, and DC between ASD and HC. Finally, functional enrichment analysis, protein-protein interaction analysis, tissue-specific expression analysis, and spatial-temporal expression trajectory were employed to reveal the biological processes, molecular functions, and developmental patterns of these related genes. The detailed analyses procedures of this study were shown in Figure 1. Given the age effects found in ASD (Padmanabhan et al., 2017), the children and adults with ASD were separately analyzed in this study.

In the formula, i and k represent the number of datasets participating in the statistics, W_i describes the square root of the sample size in the current data set, and Z is the overall statistic values across all sites obtained after Stouffer conversion.

Meta-analytic decoding of functional cognitive patterns

To decode the main functional roles involved of brain areas showing differences in ALFF, ReHo, or DC, functional characterization for each brain area was executed using the Neurosynth toolkit (https://neurosynth.org/). Specifically, the brain areas showing differences in functional measurements were divided into two types. One is the areas with increased functional measurements and the other is the areas with decreased functional measurements in ASD patients. Functional characterization of the two types of areas was separately analyzed, and the top 20 cognitive terms that showed maximum association were shown.

Associations between functional measurements and clinical performances

To explore whether the aberrant brain areas were associated with clinical performances in individuals with ASD, Pearson correlation analyses between ALFF, DC, ReHo, and autism Social Response Scale (SRS), and Autism Diagnostic Observation Scale (ADOS) scores were performed. The significant level was set at p < 0.05 with FDR correction.

Associations between functional differences and gene expression profiles

In this study, transcriptome-neuroimaging spatial association analyses were performed to link brain functional differences and gene expression profiles using Allen Human Brain Atlas (AHBA) to identify the molecular basis. We only focused on the left hemisphere to avoid sample bias because there are only two donors in the right hemisphere. Before spatial correlation analysis, we first parcellated the whole brain into 500 subregions in each hemisphere using the Schaefer atlas (Schaefer et al., 2018), and 10027 gene expression profiles were effectively detected in this atlas. A 500 × 10027 matrix was obtained where 500 and 10027 represent 500 subregions and 10027 genes. Then, the z-map resulting from the multi-site meta-analysis was also parcellated into 500 subregions using the Schaefer atlas and the mean z value in each subregion was obtained. Finally, spatial correlation between each gene expression profile and z values across the 500 subregions was performed, and the Bonferroni method with p < 0.01 was applied to identify significantly associated genes (In the subsequent enrichment analysis, the correlation coefficient r-value was further used to limit the number of genes to ensure approximately same number of genes across functional measurements, ReHo: r > 0.49, DC: r > 0.57, ALFF: r > 0.58). After identifying the significantly associated genes for each functional measurement, the top and bottom 5 genes showing the largest positive and negative correlations in children or adults were exhibited.

In addition, we identified the overlapped genes which were found in both children and adults and analyzed their expression levels to reveal the distribution in the human brain. Each gene whole-brain expression level map in MNI space was obtained from the Neurosynth database (www.neurosynth.org). Next, the whole brain was subdivided into 116 subregions using the automated anatomical labeling (AAL) atlas, and the mean gene expression level was calculated for each subregion. The positive and negative top 10 subregions with the highest gene expression level are displayed.

Gene enrichment analysis

To explore the biological functions of the co-expressed and differentially expressed genes, we performed enrichment analysis on identified genes using an online enrichment analysis tool ‘Gorilla’ (http://cbl-gorilla.cs.technion.ac.il/#ref). We used two unranked lists of genes as the running mode with the identified genes as targets and all genes (n=10027) as the background gene. The enrichment analysis results were corrected using the FDR-BH method with p < 0.05.

In addition, we used online analytical tools (http://genetics.wustl.edu/jdlab/tsea/) to delineate the specific expressions level of the genes associated with ASD in different tissues in humans. A specificity index probability (pSI = 0.05, 0.01, 0.001, and 0.0001) was applied to determine how likely a gene was specifically expressed (Xu et al., 2014). Fisher’s exact tests were used to test the significance and FDR-BH with p < 0.05 for multiple comparisons correction. Apart from this, we used online temporal-specific expression analysis (http://hbatlas.org/) to determine the specific developmental stages of the genes found both in children and adults.

Protein-protein interaction analysis

To explore whether genes associated with specific brain activity in adults with autism could construct a protein-protein interactions (PPI) network with high confidence interaction score of 0.9, we performed a PPI analysis using STRING, an online protein-protein network analysis tool (https://string-db.org/). The genes included in the PPI analysis were from the gene set with Bonferroni corrected (p < 0.01) in the related genes analyses. The top 10% of positively correlated genes and the bottom 10% of negatively correlated genes were selected as the input for PPI analysis. Genes with a degree value greater than 1×mean + 1×SD were defined as hub genes. After identifying the hub genes, the time-specific expression of hub genes was analyzed using a human transcriptome database (http://hbatlas.org/) to characterize the varying degrees of involvement of these genes in influencing cortical development and cellular processes during the life cycle (Kang et al., 2011). Moreover, we compared our results with the Simons Foundation Autism Research Initiative calibration of ASD candidate genes dataset (https://gene.sfari.org/database/human-gene/) to determine whether the identified hub genes were related to ASD.

Demographic characteristics

A total of 553 subjects from 9 centers of ABIDE I and ABIDE II (Pitt, Caltech, NYU-I, NYU-II, SU, EMC, SDSU, Yale, and USM) were included in this study. The age and gender are well matched in each center tested using two-sample t-tests and Chi-square tests, respectively. For details, please see Table 1 in the main text.

Alterations in functional activity and connectivity in ASD

Compared with HC, ASD subjects showed changed functional activity, local and global functional connectivity. The brain areas showing significant differences are presented in Table 2 and Figure 2. In detail, children with ASD showed increased ALFF in the left anterior superior temporal gyrus (STG), bilateral thalamus (THA), and subgenual anterior cingulate cortex compared with HC. In local functional connectivity, Children with ASD exhibited decreased ReHo in the bilateral posterior cingulate cortex (PCC), THA and right STG. For global functional connectivity pattern, children with ASD showed decreased DC in right STG, bilateral PCC, and medial prefrontal cortex (MPFC) while increased DC in left dorsolateral prefrontal cortex, anterior intraparietal sulcus, posterior superior temporal sulcus, anterior cingulate cortex, middle cingulate cortex, and posterior hippocampus (Figure 2A).

In adults with ASD, increased ALFF in the left middle temporal gyrus (MTG), and bilateral lateral orbitofrontal cortex while decreased ALFF in the bilateral visual cortex were found compared to HC. Adults with ASD showed increased ReHo in right MTG compared to HC. In addition, adults with ASD exhibited increased DC in left MTG, left orbitofrontal cortex while decreased DC in the bilateral medial visual cortex and sensorimotor cortex relative to HC (Figure 2B).

Moreover, by linking the brain areas showing abnormal functional measurements with behavioural domains from the Neurosynth, we found that these brain areas are primarily associated with rewarding, sensorimotor, semantic cognition and communication, locomotion, and memory (Figure 2C).

Correlation between functional measurements and clinical scores in ASD

We further explored the relationship between abnormal brain functional measurements and ASD clinical symptoms. The results revealed that ReHo in PCC was positively correlated with restricted and repetitive behavior of Autism Diagnostic Observation Schedule (ADOS: RRB) score (r = 0.3, p = 0.001), ADOS severity total (r = 0.28, p = 0.003), and ADOS total score (r = 0.29, p = 0.001) in children with ASD (Figure 3). In addition, ALFF in the left lateral orbitofrontal cortex was negatively associated with the SRS motivation score (r = -0.34, p = 0.001) in children with ASD (Figure 3).

Genes associated with abnormal functional measurements

We performed gene expression-functional measurements spatial correlation analyses, and only genes with significant correlations (p < 0.001, Bonferroni corrected) were considered. As a result, we found that expression levels of 3876 genes were correlated with ALFF, 3142 genes were correlated with ReHo, and 4169 genes were correlated with DC. The top five genes with the largest positive and negative correlations were shown (Figure S1).

Furthermore, the results from functional enrichment analysis showed that the genes associated with children's ASD were mainly responsible for the calmodulin-dependent protein kinase activity, while the genes associated with adult ASD were mainly involved in the transporter activity, voltage-gated sodium channel activity, molecular transducer activity, serine-type endopeptidase activity, etc. (Figure 4). In addition, we analyzed the tissue specificity expression of these identified genes and found that all these genes in both adults and children with ASD were mainly expressed in the human brain (Figure S2).

PPI network and hub gene

At a high confidence level of 0.9, genes related to functional activity formed a statistically significant PPI network (p = 0.00239). Furthermore, we detected 7 hub genes (SMAD3, AR, CAMK2D, GRIA1, PPARGC1A, CAMK2G, ST3GAL6 ) in the PPI network from all the identified genes stated above (Figure 5). After identifying the hub genes, we further delineated the spatiotemporal-specific expression trajectories of these hub genes with the highest degree values (Figure S3). Moreover, by comparing with ASD candidate genes database, we found that the hub genes of AR and GRIA1 have also been reported to be the ASD candidate genes.

Brain expression of the same risk genes at different ages

Finally, we identified the overlapped genes which were found to be significantly associated with functional measurements in both adults and children with ASD. Five overlapped genes including ACAN, ANK1, NPY1R, SCRT1, and VAMP1 were identified and the whole brain expression levels of these genes were mapped (Figure 6). Specifically, ACAN was most positively expressed in the right paracentral lobule, NPY1R was most positively expressed in the left amygdala and VAMP1 was most positively expressed in the right occipital lobe. The ANK1 and SCRT1 genes were most positively expressed in the vermis. ACAN was most negatively expressed in the right cerebelum_10, and NPY1R was most negatively expressed in the vermis. The gene of VAMP1 was most negatively expressed in the amygdala while SCRT1 and ANK1 were most negatively expressed in the right caudate. Moreover, we found that all five genes were significantly expressed in the amygdala at a relatively high level. By the way, we noted that the first ten brain regions with high expression of ACAN, NPY1R, VAMP1, and SCRT1 genes were almost overlapped with brain regions showing functional abnormalities in adult with ASD, especially the brain regions of right calcarine, lingual gyrus, and orbital part of middle frontal cortex.

Using a large sample dataset to establish reliable neural markers, we first explored the heterogeneous brain functional development pattern and the genetic associations for adults and children with ASD. We also confirmed that individuals with ASD showed abnormal functional activity and connectivity localized in DMN and fronto-temporal regions, and children and adults with ASD have different impaired patterns. Furthermore, transcriptome-neuroimaging association analysis linked the functional differences with gene expression levels to identify the cellar and molecular basis of functional abnormalities for ASD. Taken together, our findings revealed consistent functional changes in ASD and linked these functional alterations with gene expression levels, which provide new evidence to understand the neuropathology for ASD.

It is well known that the brain is an integrated system with different functional subsystems. DMN is one of the most important functional subsystems involved in various cognitive functions, episodic memory, emotional processing, and information integration of other brain subsystems (Andrews-Hanna et al., 2010; Li et al., 2022; Raichle, 2015). Previous studies have demonstrated that ASD has abnormal structural and functional connectivities within DMN and between DMN with other networks (Lynch et al., 2013; Nair et al., 2020; Padmanabhan et al., 2017). In our study, we revealed heterogeneous changes in functional activity and connectivity in children and adults with ASD. Specifically, we found significantly decreased local or global functional connectivity in superior temporal gyrus and DMN core brain regions of posterior cingulate cortex and medial prefrontal cortex (MPFC) in children with ASD while decreased functional activity and global connectivity in visual and sensorimotor networks in adults with ASD. The superior temporal gyrus and DMN play important roles in language and social-emotional information processing, respectively (Li et al., 2022; Wang et al., 2015). Functional disruptions in these brain areas or networks may be the underlying neural basis of behaviroal deficits in social communication, language, and emotional expression observed in autism (Abbott et al., 2016; He et al., 2018; Jou et al., 2010; Uddin et al., 2013). In adult with ASD, visual and sensorimotor networks exhibited significant functional abnormalities, which may underline abnormal visual perception and repetitive motor or impaired visuomotor integration in ASD (Chung and Son, 2020; Lepping et al., 2022; Lombardo et al., 2019). In addition, a recent study reported reduced functional connections between DMN and the primary visual cortex in early childhood with ASD (Lombardo et al., 2019). All the evidence may suggest that progressive functional deficits in ASD from DMN to visual and sensorimotor network and functional abnormality of DMN may act as a start point and eventually induced functional disruptions of other functional networks in ASD.

In addition, functional activity in subgenual anterior cingulate cortex (sgACC) and bilateral thalamus was found to significantly enhance in children with ASD. The sgACC is primarily involved in emotional control and autonomic regulation, and functional abnormalities have been widely reported in mood disorders (Cheng et al., 2022b; Drevets et al., 2008; Wu et al., 2016). In ASD, inhibited activation of sgACC was identified during attention-demanding tasks (Di Martino et al., 2009). In addition, repetitive behaviour in ASD were found to be associated with reduced FA in the white matter underlying sgACC (Thakkar et al., 2008b). These aforementioned findings indicated that functional impairment of sgACC in ASD may be related to inappropriate emotional response triggering repetitive behaviors (Thakkar et al., 2008a). The thalamus, as a crucial sensorimotor relay brain region, has an atypical developmental trajectory in ASD childhood while the trajectory overlaps or intersects with the typical development during adulthood (McLaughlin et al., 2018), which may be the cause that we do not find thalamic abnormalities in adult ASD.

Meanwhile, the global functional connectivity of dorsolateral prefrontal cortex, anterior intraparietal sulcus, posterior middle temporal gyrus, posterior hippocampus, and middle cingulate was higher than that of healthy controls. The dorsolateral prefrontal cortex, anterior intraparietal sulcus, and posterior middle temporal gyrus are the core areas of dorsal attention network (DAN) maintaining attention stability (Corbetta and Shulman, 2002). DAN and DMN play important roles in external and internal oriented cognitive functions respectively (Spreng et al., 2013). Thus, the increased global functional connectivity of this network may be a compensatory for attention deficits in ASD (Fitzgerald et al., 2015). The middle cingulate cortex is associated with social cognition, pain perception (Christakou et al., 2013). This area also participates in working memory together with the posterior hippocampus (Beckmann et al., 2009; Robinson et al., 2015; Zhang et al., 2021). Increased global brain connectivity in both regions may also related to compensatory for memory and social perception problems found in autism (Pfeifer et al., 2013). Increased functional activity, local or global functional connectivity in middle temporal gyrus and lateral orbitofrontal cortex were observed in adults with ASD. The lateral orbitofrontal cortex has dense connections with amygdala and ACC and plays a crucial role in social and emotional processing, especially decision-making in social environments (Ghashghaei et al., 2007). The abnormal cell structure and increased neuron density were found in this area in adult ASD (Rolls and Grabenhorst, 2008). Middle temporal gyrus has been reported to be associated with speech processing, emotional responses, and social cognition (Davey et al., 2016; Takano and Nomura, 2022; Xu et al., 2019a; Xu et al., 2015). The abnormal functional activity and connectivity was supported by our previous study which found changed functional connectivity of different subregions of middle temporal gyrus in ASD (Xu et al., 2019b). Taken together, all the increased functional activity, local or global functional connectivity found in our study may provide a compensatory mechanism for functional disruptions found in ASD.

The associations between functional abnormalities and gene transcriptome profiles in ASD were established. The identified genes are primarily involved in ion channels or receptors, neurotransmitter synthesis or release, and synaptic ion channel control, which affect signal transduction between neurons in ASD. The previous researches have revealed that disruption of the balance of synaptic excitation and inhibition in specific cortical areas of the brain is a key pathological mechanism of autism (Sohal and Rubenstein, 2019; Vattikuti and Chow, 2010). Thus, we speculated that abnormal neuronal excitability or inhibition during signaling may lead to abnormal brain activity and connectivities found in autism.

Protein-protein interaction is crucial for understanding cell physiology in normal and disease states by delineating physical contacts between proteins in the cell. In our study, using PPI analysis of genes associated with functional abnormalities in ASD, seven hub genes for neural signal transmission were identified with high confidence. Among these hub genes, the genes of CAMK2 and GRIA1 which play key roles in the signal transmission between neurons have been widely reported in ASD. The CAMK2G is involved in formation and plasticity maintenance of neuronal synapses, and abnormal expression leads to impaired neuron maturation (Proietti Onori et al., 2018; Zhu et al., 2021). The GRIA1 is involved in regulation of inhibitory neurotransmitter receptors (glutamate receptor ANAR) (Montanari et al., 2022), and the decreased synaptic transmission activity of inhibitory signals can lead to the imbalance of neuronal excitation/inhibition (Hwang et al., 2022). Thus, CAMK2 and GRIA1 genes may be the molecular basis for functional impairments in ASD. In addition, AR was also identified as a hub gene in our study. The mutation of AR gene may lead to androgen insensitivity (Holterhus et al., 1999). Abnormal androgen secretion increases susceptibility to neurodevelopmental disorders such as ASD and intellectual disabilities (Quartier et al., 2018), which may explain why autism is more common in males than females.

Interestingly, all the overlapped genes found in both children and adult with ASD showed high expression level in amygdala. The amygdala is an important node of limbic system and plays an important role in emotion regulation and social processes (Linhartová et al., 2019). Compared with healthy controls, increased amygdala volume and disrupted microstructure of amygdala to cortex tracts in ASD patients were identified (Gibbard et al., 2018). However, the finding of abnormal amygdala volume in ASD is not consistent (Haar et al., 2016; Kim et al., 2010; Mosconi et al., 2009; van Rooij et al., 2018). In addition, increased spine density and decreased dendritic growth in amygdala and reduced amygdala–prefrontal functional connectivity in ASD were also found (Ibrahim et al., 2019; Weir et al., 2018). The amygdala was also reported to be associated with anxiety in ASD (Andrews et al., 2022). All the evidence highlights the important role of amygdala in ASD and provides potential neural-transcriptional mechanisms underlying the neuropathology of autism.

Limitations

There are some limitations in this study. First, the data set we used was downloaded from multiple centers. Although we used multi-center meta-analysis to avoid potential effects, the heterogeneity of the scan parameters and patients clinical symptoms may still affect our results. The explanation of our findings needs to be caution. Second, there are more males with ASD than females with ASD in this study, whether the brain areas showed significant gender differences in ASD and whether male and female ASD subjects showed different brain development patterns need to be further explored. Third, although the AHBA dataset was applied to link genes’ expression to functional abnormalities in ASD, whether the identified genes and biological processed really related to neuropathology of ASD needs to be further validated in animal experiments.

Acknowledgment

This work is supported by grants from the National Science Foundation (62176044 and 62006038), Natural Science Foundation of Yunnan Province (nos. 202001BC070001 and 202102AA100053), and Yunnan Fundamental Research Projects (grant NO. 202201BE070001-004), and Sichuan Science and Technology Program (2022YFS0180).

Conflict of interest statement

There has no any conflict of interest.

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Table 1 The demographic information of the used subjects.

Table 2. Significant group differences in amplitude of low-frequency fluctuation (ALFF), local regional homogeneity (ReHo) and degree centrality (DC) between children with ASD and HC, and between adults with ASD and HC

The authors have declared there is NO conflict of interest to disclose

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Delineation of Functional Changes and Associated Cortical Transcriptomic Profiles for Autism Spectrum Disorders

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