Neuroanatomical Basis of Language Ability in an Autism Subgroup with Moderate Language Deficits

Neuroanatomical Basis of Language Ability in an Autism Subgroup with Moderate Language Deficits | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Neuroanatomical Basis of Language Ability in an Autism Subgroup with Moderate Language Deficits Yaqiong Xiao, Ningxuan Zhang, Shuiqun Zhang, Kaiyu Huang, Jin Xin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4673621/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Nov, 2024 Read the published version in European Child & Adolescent Psychiatry → Version 1 posted 9 You are reading this latest preprint version Abstract Children with autism spectrum disorder (ASD) are highly heterogenous in their language abilities. A number of studies have shown neural correlates of language deficits in children with ASD, but the underlying neuroanatomical foundation of early language deficits in ASD remains largely elusive. In this study, we analyzed MRI data from a cohort of Chinese children with ASD (n = 67) and typical development (TD, n = 37) aged 1.5 to 6.5 years. The ASD sample was classified into two subgroups based on the median of the language scores: ASD with moderate language deficits (ASD moderate , n = 34) and ASD with severe language deficits (ASD severe , n = 34). We tested the group differences in the brain volumes between TD and two ASD subgroups, and also examined the associations between cortical grey matter volume and language abilities in TD and ASD subgroups, separately. We observed significant group differences in grey matter and white matter volume, with post-hoc analyses specifically indicating significant differences between TD and ASD moderate subgroup. Significant correlations between grey matter volume and language scores were observed exclusively within the ASD moderate subgroup, including positive associations in the bilateral superior temporal gyrus, hippocampus, and left inferior parietal lobe, and negative correlations in the bilateral precuneus. These findings provide novel evidence for the neuroanatomical basis related to language ability in an ASD subgroup with moderate language deficits, and offer new insights into the heterogeneity of language deficits in children with ASD. Autism spectrum disorder young children language ability grey matter volume superior temporal gyrus hippocampus Figures Figure 1 Figure 2 Figure 3 Introduction Individuals with autism spectrum disorder (ASD) exhibit significant heterogeneity in their language abilities. Some individuals with ASD demonstrate language skills that are nearly within the typical range or minor language impairments, while a considerable proportion of individuals with ASD remain minimally verbal, experiencing profound challenges in expressive and receptive language abilities [ 1 – 4 ]. This wide spectrum of language functioning within the ASD population underscores the complexity of understanding language deficits in this disorder. Functional magnetic resonance imaging (fMRI) studies have provided insights into the neural underpinnings of language impairments in ASD, such as abnormal inter-hemispheric functional connectivity between language-related regions [ 5 ], reduced neural activation in language regions [ 6 – 10 ], and aberrant connectivity patterns between temporal cortex and visual region [ 11 , 12 ]. Collectively, these findings demonstrate the multifaceted nature of language impairments in ASD and highlight the importance of understanding the complex interplay between neural circuitry and language function in this population. In recent years, investigations into neuroanatomical alterations associated with ASD have consistently reported structural abnormalities in young children with ASD as compared to typically developing (TD) controls [ 13 – 19 ]. These studies underscore the presence of distinct neuroanatomical differences associated with ASD pathology. Moreover, some studies have shown the behavioral relevance of these ASD-related structural alterations in young children [ 14 , 15 , 18 , 20 ], further emphasizing the significance of understanding the neural underpinnings of ASD symptoms. A few studies have also demonstrated the relationships between brain morphological features and language ability in school-age children with ASD [ 21 – 23 ] and in preschool children with ASD [ 18 , 24 ]. For example, a recent study reported grey matter thickness and gyrification of language‑related areas were related to language abilities in children with ASD [ 25 ]. Arutiunian et al. [ 23 ] demonstrated diminished grey matter volume in the bilateral amygdala and hippocampus among school-age children with ASD. Joseph et al. [ 18 ] found less left-lateralized asymmetry in language-related regions, and the reduced leftward asymmetry of language region (i.e., pars opercularis) was correlated with better language abilities in children with ASD. The study by Naigles et al. [ 24 ] divided a group of preschool boys with ASD into three subgroups based on their language abilities and reported that the subgroups differed in the bilateral inferior longitudinal fasciculus. Additionally, they found that the fractional anisotropy along this fiber tract significantly correlated with language scores across the ASD subgroups [ 24 ]. Our recent study reported associations between grey matter volume of prefrontal cortex and cerebellum and language and social abilities in children without ASD, yet these associations were notably absent in children with ASD [ 16 ]. Despite these explorations, the underlying neuroanatomical foundation of early language deficits in ASD remains largely elusive, which leads to challenges in understanding the heterogeneity of language functions in ASD and its underlying mechanisms. In the present study, we aimed to investigate the neuroanatomical basis related to language deficits in children with ASD and varying language deficits. We collected structural MRI data from a relatively large cohort (69 ASD/38 TD) of young Chinese children aged 1.5–6.5 years. Within the ASD group, children were divided into two subgroups based on the median of their language scores. First, we compared global brain volumes and regional grey matter volume between TD controls and two ASD subgroups to identify any significant group differences. Our hypothesis posited that ASD subgroups would exhibit greater grey matter volume compared to the TD group. Subsequently, we investigated the correlations between language scores and grey matter volume within the TD group and two ASD subgroups separately. We anticipated distinct correlation patterns within the ASD subgroups reflecting their varying language deficits. Given previous findings demonstrated associations between language scores and grey matter volume in non-ASD children [ 16 ], we anticipated there would be similar correlation patterns in the TD group. Methods Participants In this study, we recruited a total of 107 participants (69 ASD/38 TD) aged 1.5 to 6.5 years from the Foshan Fosun Chancheng Hospital, Foshan, China between November 2021 and May 2023. All participants completed the Gesell Development Schedule (GDS), which measures various developmental domains including fine motor, gross motor, personal-social, language, and adaptive behavior [ 26 ]. The parents or guardians of all participants completed the Autism Behavior Checklist (ABC), a widely used questionnaire for assessing autistic behaviors and symptom severity in individuals with ASD [ 27 ]. Although the ABC was not utilized for diagnosing ASD, it is notable that all TD children had an ABC score < 44, indicating the absence of ASD. Conversely, children with ASD had ABC scores ≥ 53. All participants diagnosed with ASD met the DSM-V criteria for ASD through clinical interview and underwent either the Autism Diagnostic Observation Schedule (ADOS Module 1 or 2) or the Childhood Autism Rating Scale (CARS), administered by the same clinician. TD participants had a total score > 85 on the GDS, indicating normal development. All children were native Mandarin or Cantonese speakers with normal hearing and no family history of mental or psychiatric disorders. This study was approved by the Foshan Fosun Chancheng Hospital. Informed consent was obtained from parents or guardians of all participants. Two participants were excluded from the analysis due to poor MRI data quality (1 ASD/1 TD). Given the diverse range of language abilities observed within the remaining ASD sample, we stratified the ASD participants into two subgroups based on the median of their language scores obtained from the GDS. Specifically, ASD participants were classified as ASD with moderate language deficits (ASD moderate , n = 34) if they had a language score ≥ 44.4, while those with a language score < 44.4 were categorized as ASD with severe language deficits (ASD severe , n = 34). The detailed demographic, clinical, and behavioral information of TD group and two ASD subgroups is summarized in Table 1. Table 1. Demographic details and clinical and behavioral testing scores. TD (n = 37) ASD moderate (n = 34) ASD severe (n = 34) Mean±SD Range Mean±SD Range Mean±SD Range Age (years) 3.39±1.49 1.5-5.87 3.31±1.33 1.52-6.53 3.039±0.98 2-5.57 Gender (M/F) 32/5 32/2 25/9 Gesell subscale scores * Gross motor 97.42±6.1 81.1-107 77.76±8.7 47.8-91 71.79±8.34 52-87.5 Fine motor 96.73±5.34 86-108 73.31±9.39 54.1-90.4 64.25±10.21 45.9-84.1 Personal-social 93.76±4.52 80-103 60.46±7.4 31-70.2 48.16±7.78 30.2-64.2 Language 91.47±5.55 75.7-101 55.07±7.76 44.6-70.7 37.11±5.91 23.6-44.2 Adaptive behavior 93.79±5.39 80-107 66.18±8.76 40.9-82 55.07±13.33 31.6-93.7 Total 94.34±4.22 85.1-100.8 66.72±5.61 49.1-79.4 55.25±7.11 39.3-68.74 ABC 25.3±8.56 8-43 66.65±14.26 53-107 74.97±19.81 54-130 ADOS scores # ADOS SA 12.41±3.6 7-20 13.04±3.26 8-22 ADOS RRB 1.24±0.91 0-4 1.52±1.05 0-4 ADOS Total 13.66±4.08 8-23 14.56±3.91 9-24 CARS Total ## 14.33±1.15 13-15 31.92±2.02 30-38 35±4.97 25-45 Abbreviations: ABC, Autism, Behavior Checklist; RRB, Restricted and Repetitive Behavior; ADOS, Autism Diagnostic Observation Schedule; CARS, # ADOS was administrated in 54 ASD children (ASD moderate : n = 29; ASD severe : n = 25). ## CARS was administrated in 8 TD children and 60 ASD children (ASD moderate : n = 26; ASD severe : n = 34). MRI data acquisition Before MRI scanning, all of the participants were administered 0.5% chloral hydrate 0.5 ml/kg (maximum dose 10 ml) orally to induce and maintain sleep. All participants continued sleeping during scanning. All the structural MRI data were collected on a 3.0T SEMENS Skyra at the Foshan Chancheng Hospital, Foshan, China using a T1-weighted MPRAGE sequence (TE = 2.98 ms, TR = 2300 ms, resolution = 1.0 x 1.0 x 1.0 mm 3 , space gap=0, slice thickness = 1 mm, flip angle =9°, 144 slices, a total of 5 min 9 s). Imaging data preprocessing Prior to preprocessing, MR images were visually inspected and then normalized to standard AC/PC orientations. To extract grey matter maps, MRI data were processed with the Voxel-based morphometry (VBM) pipeline using the Computational Anatomy Toolbox (CAT 12; https://neuro-jena.github.io/cat/) for Statistical Parametric Mapping (SPM12; http://www.fil.ion.ucl.ac.uk), running in Matlab R2020a (MathWorks, Natick, MA, USA). Here, to minimize the potential confounds introduced by the different brain sizes and tissues between young children and adults [28], customized pediatric tissue probability maps and the diffeomorphic anatomical registration through exponentiated lie algebra (DARTEL) templates were created with the Cerebromatic (COM) Toolbox [29, 30] which provides regression parameters modeled with 1914 healthy participants aged 13 months to 75 years. Specifically, the COM toolbox can be used to generate the custom tissue probability maps that matches sample demographics to parameters that influence brain structure using a flexible non-parametric approach: multivariate adaptive regression splines [29]. The custom DARTEL templates can also be created using the COM toolbox, which matches sample demographics to a second set of regression parameters derived from 1919 participants in the same databases [30]. Here, the age and sex of each participant, and the field strength were entered into the COM toolbox to create the custom tissue probability maps and DARTEL templates, separately. For the VBM analysis, MRI images were segmented into grey matter, white matter, and cerebrospinal fluid (CSF). Following segmentation, the grey matter images were affine registered to pediatric tissue probability maps previously generated, and then they were spatially normalized to a study-specific pediatric template using DARTEL registration. Subsequently, the grey matter images were modulated with Jacobian determinants from the normalization process to preserve regional volumes. Quality control measures were implemented to ensure sample homogeneity, with no outlier images identified. The grey matter images underwent smoothing using an 8 mm full-width at half-maximum (FWHM) kernel. Processed grey matter images had a voxel size of 1.5 mm × 1.5 mm × 1.5 mm. Finally, total grey matter, white matter, CSF, and intracranial volume (TIV) measurements were extracted for each participant. Statistical analysis Group differences in demographic and clinical data Statistical analyses for demographic and clinical data were performed using R software (version 4.1.2). Specifically, differences between the TD group and the two ASD subgroups in demographic information (i.e., age) and behavioral testing (total and domain scores of GDS) were assessed using two-sample t -tests, while differences in gender were assessed using the Chi-square test. Comparisons of clinical (e.g., ADOS, CARS, ABC) scores were conducted only between the two ASD subgroups. Results for age and total and domain scores of GDS were corrected for multiple comparisons using the false discovery rate (FDR) approach. Comparisons of global volumes between TD and two ASD subgroups We analyzed the group differences among the TD group and the ASD subgroups in volumes of grey matter, white matter, CSF using one-way analysis of covariance (ANCOVA), while controlling for age, gender, and TIV. Additionally, a group comparison of TIV was conducted while controlling for age and gender. The results were corrected for multiple comparisons using the FDR correction ( p < 0.05). For significant group differences, post-hoc t -tests were conducted to further explore the differences between TD, ASD moderate , and ASD severe , with corrections for multiple testing using the FDR approach. Group differences in regional grey matter volume The whole brain voxel-wise analyses were performed to examine the group differences between TD and two ASD subgroups in grey matter volume using the ‘y_TTest2_Image’ function in DAPBI (a toolbox for Data Processing & Analysis for Brain Imaging; https://rfmri.org/dpabi) [31], with age, gender, and TIV as covariates. We used a binary grey matter mask for the group analysis that was created with all the grey matter images across participants. Results were corrected at the cluster-level using Gaussian random field (GRF) theory, in which the t map was converted to the z map (|Z| ≥ 3.29, voxel-wise p 800 voxels, cluster-wise p < 0.05, two-tailed).  Correlations between grey matter volume and language ability Further, we examined the correlations between whole-brain grey matter volume and language ability in TD and two ASD subgroups separately, using the ‘y_Correlation_Image’ function in DPABI, while controlling for age, gender, and TIV. The resulting correlation maps were converted to the z maps and corrected for multiple comparisons at the cluster-level using the GRF correction (|Z| ≥ 3.29, voxel-wise p 650 voxels, cluster-wise p < 0.05, two-tailed). Finally, all the significant clusters were visualized with the BrainNet Viewer (http://www.nitrc.org/ projects/bnv/). Results Results of group comparisons in demographic and clinical data No differences were observed at age or gender between TD and two ASD subgroups or between two ASD subgroups after correcting for multiple comparisons ( p s > 0.05). As shown in Figure 1, there were significant differences in all domains measured by the GDS between TD and two ASD subgroups ( p s < 0.001), and between two ASD subgroups ( p s 0.1). Significant differences in total brain volumes As shown in Figure 2, significant differences between TD and two ASD subgroups were observed in total volumes of grey matter ( F (2, 99) = 33.22, p < 0.001) and white matter ( F (2, 99) = 6.96, p = 0.001), but not in total volume of CSF ( F (2, 99) = 0.76, p = 0.44), controlling for age, gender, and TIV. There were differences between ASD moderate and ASD severe subgroups in TIV ( F (1, 63) = 3.71, p = 0.028) when controlling for age and gender. However, this result was not significant after controlling for multiple comparisons using the FDR method. Post-hoc analyses only showed significant group differences in grey matter volume between the TD group and ASD moderate subgroup ( p = 0.047, Cohen’s d = 0.55, 95% CI [0.07, 1.03]). No significant differences in regional grey matter volume between TD and two ASD subgroups The whole-brain voxel-wise analysis did not show significant group differences in grey matter volume between TD and two ASD subgroups after correcting for multiple comparisons with the GRF method (|Z| ≥ 3.29, voxel-wise p 800 voxels, cluster-wise p < 0.05, two-tailed). Significant grey matter volume–language correlations in the ASD moderate subgroup We observed significant correlations between grey matter volume and language scores in the ASD moderate subgroup controlling for age, gender, and TIV. Specifically, there were positive associations in the left superior/inferior temporal gyrus (STG/ITG) ( r = 0.7, p < 0.001), right STG ( r = 0.71, p < 0.001), bilateral hippocampus extending to parahippocampal gyrus (left: r = 0.49, p = 0.005; right: r = 0.55, p = 0.0015), and left inferior parietal lobe (IPL) ( r = 0.55, p = 0.0015), and negative correlations in the bilateral precuneus ( r = -0.55, p = 0.0015) (see Figure 3 and Table 2). However, no significant correlations were observed in the ASD severe subgroup or TD group. All the resulting clusters were corrected for multiple comparisons (|Z| ≥ 3.29, voxel-wise p 650 voxels, cluster-wise p < 0.05, two-tailed). Table 2. Clusters showed significant correlations with Gesell language scores in the ASD moderate subgroup. Region Peak MNI Peak Z value Voxel size (mm 3 ) X Y Z Left hippocampus, parahippocampal gyrus -29 -30 -12 6.55 2579 Left superior temporal gyrus, inferior temporal gyrus -56 -65 -2 6.3 2453 Right superior temporal gyrus 59 -35 6 5.49 1058 Right hippocampus, parahippocampal gyrus 29 -21 -9 5.19 874 Left inferior parietal lobe -48 -30 45 4.65 689 Bilateral precuneus 8 -59 29 -4.1 844 Discussion In the present study, we stratified ASD children into moderate and severe language deficits subgroups to capture the heterogeneity in language ability among the ASD sample. We observed significant group differences in grey matter volume, with post-hoc analyses specifically indicating significant disparities between the TD group and ASD moderate subgroup. Moreover, significant correlations between grey matter volume and language scores were observed exclusively within the ASD moderate subgroup, included positive associations in the bilateral STG, hippocampus, and left IPL, and negative correlations in the bilateral precuneus. These findings suggest potential neural underpinnings related to the specific neurodevelopmental profile of individuals with moderate language deficits in ASD. Notably, we observed significantly positive correlations between grey matter volume in the bilateral STG and language scores in the ASD moderate subgroup. This finding aligns with previous research highlighting the role of the STG in language processing [32–34] and its structural abnormalities in individuals with ASD [13, 35, 36]. The STG is a critical region for language comprehension and processing and has been extensively studied in the context of typical language development [37–39]. Moreover, a number of fMRI studies have consistently demonstrated reduced activation of the bilateral STG during speech processing in young children with ASD [6–10]. While research has reported reduced grey matter of the STG in individuals with ASD compared to controls [35, 36], associations with language deficits have not been documented. Our findings extend prior observations by demonstrating that within a specific subgroup of ASD individuals with moderate language deficits, there is a clear relationship between grey matter volume of the STG and language abilities. This suggests that the degree of grey matter reduction in the STG may serve as a biomarker for the severity of language impairment in ASD with moderate language deficits. Additionally, we found a significant positive correlation between grey matter volume in the bilateral hippocampus and language scores in the ASD moderate subgroup. The hippocampus is traditionally associated with memory and spatial navigation; however, recent studies have highlighted its involvement in language processing and development [40–44]. Notably, recent research has demonstrated reduced grey matter volume in the hippocampus and its associations with language deficits in children with ASD [21, 45]. Our findings provide further evidence for these associations, suggesting that in children with ASD—particularly those with moderate language deficits—the hippocampus may play a crucial compensatory role in supporting language function. The observed correlations may reflect the hippocampus's role in facilitating language learning and memory retrieval processes, which are often impaired in ASD [45, 46]. Previous neuroimaging studies have shown reduced hippocampal volumes in individuals with ASD, which correlates with the severity of cognitive and language deficits [23, 47]. Our results add to this body of work by indicating that within a subgroup of children with moderate language deficits, those with greater hippocampal grey matter volume exhibit better language abilities. Furthermore, our data demonstrated significant associations between grey matter volume in the left IPL and language scores in the ASD moderate subgroup. The IPL plays a critical role in language processing, as it is involved in phonological processing and the integration of sensory information necessary for language comprehension and production [48]. It has also been implicated in various aspects of language function, including syntactic processing and semantic integration [32, 49]. Research has shown that grey matter volume in the left parietal lobe is associated with delayed language development in children with ASD [50]. Research found that children with ASD exhibit increased grey matter volume in language-related regions, including the left parietal lobe [51]. This increase could reflect compensatory mechanisms or neural plasticity as the brain attempts to adapt to and mitigate language difficulties in individuals with ASD. Such neural plasticity might be particularly pronounced in children with moderate language deficits, suggesting that their brains are actively reorganizing to support language functions despite the challenges posed by ASD. Despite the presence of significant correlations between grey matter volume and language abilities in ASD children with moderate language deficits, such correlations were absent in ASD children with severe language deficits and TD children. This suggests that the relationship between grey matter volume and language abilities varies across different stages of development. ASD children with moderate language deficits might be at a developmental stage where grey matter alterations have a more pronounced impact on language skills. In contrast, ASD children with severe language deficits and TD children may follow different developmental trajectories regarding brain-behavior relationships. This finding highlights heterogeneity within the ASD population. ASD children with moderate language deficits may represent a subgroup with specific neuroanatomical characteristics that predispose them to language impairments. Conversely, ASD children with severe language deficits may have different underlying neurobiological mechanisms contributing to their language difficulties, which may not be captured by grey matter volume alone. It is also possible that the absence of the brain-behavior relationships in other groups may be due to relatively smaller individual variability within the TD group (SD = 5.55) and the ASD severe subgroup (SD = 5.996) compared to the ASD moderate subgroup (SD = 7.76). Limitations A few limitations of the present study should be noted. First, the cross-sectional design limits our ability to infer causality between grey matter volume and language abilities. Longitudinal studies are needed to determine whether changes in brain structure over time are directly associated with changes in language skills, particularly in identifying how early differences in grey matter volume might predict future language development. Second, the sample size here was relatively small, which may affect the generalizability of the findings. Future research should include larger cohorts to validate these results and provide more robust conclusions. Third, the stratification of subgroups may be biased by the sample included in this study, as the heterogeneity of language abilities within the ASD population could have varied effects on subgroup classifications. Finally, while this study classified ASD children into moderate and severe language deficit subgroups, further stratification or additional subgroups could provide a more nuanced understanding of the neuroanatomical variations associated with different levels of language impairment in ASD. Conclusion This study highlights the significant heterogeneity in language abilities among children with ASD and underscores the complex neuroanatomical foundations of early language deficits. By examining a cohort of Chinese children with ASD and TD, we identified distinct differences in grey matter and white matter volumes, particularly in grey matter volume between the TD group and the ASD moderate subgroup. Notably, we found significant correlations between grey matter volume and language scores exclusively within the ASD moderate subgroup. These correlations included positive associations in regions such as the bilateral STG, hippocampus, and left IPL, and negative correlations in the bilateral precuneus. Our findings provide novel evidence linking specific brain regions to language abilities in ASD children with moderate language deficits, offering new insights into the neuroanatomical basis of language deficits and contributing to a better understanding of the heterogeneity observed in ASD. This research underscores the importance of considering subgroup variations in understanding the complexity of the condition and highlights the need to account for subgroup-specific neuroanatomical characteristics when developing targeted interventions for language deficits in children with ASD. Declarations Acknowledgements We are grateful to the parents and children who participated in our research; without them, this work would not be possible. Author contributions Y.X. and A.Y. designed the study. A.Y., S.Z., J.X., and Q.H. recruited the participants and collected the data. Y.X., N.Z., and K.H. analysed the data. Y.X. drafted the main manuscript. All authors reviewed the manuscript and contributed to manuscript editing and revision. Funding This study was supported by the National Natural Science Foundation of China (32200808), Research Capability Improvement Project of Guangzhou Medical University (2024150), and Research Fund Project of Guangdong Provincial Bureau of Traditional Chinese Medicine (20221372). Conflict of interests The authors declare no conflict of interests. Data availability The tidy data used for the analyses reported in this article are available at https://github.com/Yaqiongxiao/asdGM.subgroups. References Tager-Flusberg H, Kasari C (2013) Minimally verbal school-aged children with autism spectrum disorder: The neglected end of the spectrum. Autism Res 6:468–478. https://doi.org/10.1002/aur.1329 Anderson DK, Lord C, Risi S, et al (2007) Patterns of Growth in Verbal Abilities Among Children With Autism Spectrum Disorder. J Consult Clin Psychol 75:594–604. https://doi.org/10.1037/0022-006X.75.4.594 Kjelgaard MM, Tager-Flusberg H (2001) An investigation of language impairment in autism: Implications for genetic subgroups. Lang Cogn Process 16:287–308. https://doi.org/10.1080/01690960042000058 Pickles A, Anderson DK, Lord C (2014) Heterogeneity and plasticity in the development of language: A 17-year follow-up of children referred early for possible autism. J Child Psychol Psychiatry Allied Discip 55:1354–1362. https://doi.org/10.1111/jcpp.12269 Dinstein I, Pierce K, Eyler L, et al (2011) Disrupted Neural Synchronization in Toddlers with Autism. Neuron 70:1218–1225. https://doi.org/10.1016/J.NEURON.2011.04.018 Xiao Y, Wen TH, Kupis L, et al (2022) Neural responses to affective speech, including motherese, map onto clinical and social eye tracking profiles in toddlers with ASD. Nat Hum Behav 2022 6:443–454. https://doi.org/10.1038/s41562-021-01237-y Lombardo M V., Pierce K, Eyler LT, et al (2015) Different functional neural substrates for good and poor language outcome in autism. Neuron 86:267–277. https://doi.org/10.1016/j.neuron.2015.03.023 Lombardo M V, Pramparo T, Gazestani V, et al (2018) Large-scale associations between the leukocyte transcriptome and BOLD responses to speech differ in autism early language outcome subtypes. Nat Neurosci 21:1680–1688. https://doi.org/10.1038/s41593-018-0281-3 Eyler LT, Pierce K, Courchesne E, et al (2012) A failure of left temporal cortex to specialize for language is an early emerging and fundamental property of autism. Brain 135:949–960. https://doi.org/10.1093/brain/awr364 Redcay E, Courchesne E (2008) Deviant Functional Magnetic Resonance Imaging Patterns of Brain Activity to Speech in 2–3-Year-Old Children with Autism Spectrum Disorder. Biol Psychiatry 64:589–598. https://doi.org/10.1016/J.BIOPSYCH.2008.05.020 Gao Y, Linke A, Jao Keehn RJ, et al (2019) The language network in autism: Atypical functional connectivity with default mode and visual regions. Autism Res 12:1344–1355. https://doi.org/10.1002/aur.2171 Xiao Y, Wen TH, Kupis L, et al (2023) Atypical functional connectivity of temporal cortex with precuneus and visual regions may be an early-age signature of ASD. Mol Autism 14:1–11. https://doi.org/10.1186/s13229-023-00543-8 Retico A, Giuliano A, Tancredi R, et al (2016) The effect of gender on the neuroanatomy of children with autism spectrum disorders: A support vector machine case-control study. Mol Autism 7:1–20. https://doi.org/10.1186/s13229-015-0067-3 Wang J, Fu K, Chen L, et al (2017) Increased gray matter volume and resting-state functional connectivity in somatosensory cortex and their relationship with autistic symptoms in young boys with autism spectrum disorder. Front Physiol 8:588. https://doi.org/10.3389/FPHYS.2017.00588/XML/NLM Cai J, Hu X, Guo K, et al (2018) Increased Left Inferior Temporal Gyrus Was Found in Both Low Function Autism and High Function Autism. Front Psychiatry 9:542. https://doi.org/10.3389/FPSYT.2018.00542/BIBTEX Xin J, Huang K, Yi A, et al (2023) Absence of associations with prefrontal cortex and cerebellum may link to early language and social deficits in preschool children with ASD. Front Psychiatry 14:1144993. https://doi.org/10.3389/fpsyt.2023.1144993 Lucibello S, Verdolotti T, Giordano FM, et al (2019) Brain morphometry of preschool age children affected by autism spectrum disorder: Correlation with clinical findings. Clin Anat 32:143–150. https://doi.org/10.1002/CA.23252 Joseph RM, Fricker Z, Fenoglio A, et al (2014) Structural asymmetries of language-related gray and white matter and their relationship to language function in young children with ASD. Brain Imaging Behav 8:60–72. https://doi.org/10.1007/s11682-013-9245-0 Raznahan A, Lenroot R, Thurm A, et al (2013) Mapping cortical anatomy in preschool aged children with autism using surface-based morphometry. NeuroImage Clin 2:111–119. https://doi.org/10.1016/j.nicl.2012.10.005 Lucibello S, Bertè G, Verdolotti T, et al (2021) Cortical Thickness and Clinical Findings in Prescholar Children With Autism Spectrum Disorder. Front Neurosci 15:776860. https://doi.org/10.3389/FNINS.2021.776860 Arutiunian V, Gomozova M, Minnigulova A, et al (2023) Structural brain abnormalities and their association with language impairment in school-aged children with Autism Spectrum Disorder. Sci Rep 13:1172. https://doi.org/10.1038/S41598-023-28463-W/FIGURES/8 Sharda M, Foster NEV, Tryfon A, et al (2017) Language Ability Predicts Cortical Structure and Covariance in Boys with Autism Spectrum Disorder. Cereb cortex 27:1849–1862. https://doi.org/10.1093/CERCOR/BHW024 Arutiunian V, Davydova E, Pereverzeva D, et al (2023) Reduced grey matter volume of amygdala and hippocampus is associated with the severity of autistic symptoms and language abilities in school-aged children with Autism Spectrum Disorder: an exploratory study. Brain Struct Funct 228:1573–1579. https://doi.org/10.1007/s00429-023-02660-9 Naigles LR, Johnson R, Mastergeorge A, et al (2017) Neural Correlates of Language Variability in Preschool-Aged Boys with Autism Spectrum Disorder. Autism Res 10:1107. https://doi.org/10.1002/AUR.1756 Arutiunian V, Gomozova M, Minnigulova A, et al (2023) Structural brain abnormalities and their association with language impairment in school-aged children with Autism Spectrum Disorder. Sci Rep 13:1–15. https://doi.org/10.1038/s41598-023-28463-w Yang Y (2016) Rating Scales For Children’s Developmental Behavior and Mental Health, 1st ed. People’s Medical Publishing House, Beijing Krug DA, Arick J, Almond P (1980) Behavior checklist for identifying severely handicapped individuals with high levels of autistic behavior. J Child Psychol Psychiatry 21:221–229. https://doi.org/10.1111/J.1469-7610.1980.TB01797.X Yoon U, Fonov VS, Perusse D, Evans AC (2009) The effect of template choice on morphometric analysis of pediatric brain data. Neuroimage 45:769–777. https://doi.org/10.1016/J.NEUROIMAGE.2008.12.046 Wilke M, Altaye M, Holland SK (2017) CerebroMatic: A versatile toolbox for spline-based MRI template creation. Front Comput Neurosci 11:5. https://doi.org/10.3389/fncom.2017.00005 Wilke M (2018) A spline-based regression parameter set for creating customized DARTEL MRI brain templates from infancy to old age. Data Br 16:959–966. https://doi.org/10.1016/J.DIB.2017.12.001 Yan CG, Wang X Di, Zuo XN, Zang YF (2016) DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Neuroinformatics 14:339–351. https://doi.org/10.1007/S12021-016-9299-4 Friederici AD (2011) The brain basis of language processing: from structure to function. Physiol Rev 91:1357–1392. https://doi.org/10.1152/PHYSREV.00006.2011 Hickok G, Poeppel D (2007) The cortical organization of speech processing. Nat Rev Neurosci 8:393–402. https://doi.org/10.1038/nrn2113 Redcay E (2008) The superior temporal sulcus performs a common function for social and speech perception: implications for the emergence of autism. Neurosci Biobehav Rev 32:123–142. https://doi.org/10.1016/J.NEUBIOREV.2007.06.004 Liu J, Yao L, Zhang W, et al (2017) Gray matter abnormalities in pediatric autism spectrum disorder: a meta-analysis with signed differential mapping. Eur Child Adolesc Psychiatry 26:933–945. https://doi.org/10.1007/S00787-017-0964-4 Toal F, Daly EM, Page L, et al (2010) Clinical and anatomical heterogeneity in autistic spectrum disorder: A structural MRI study. Psychol Med 40:1171–1181. https://doi.org/10.1017/S0033291709991541 Xiao Y, Friederici AD, Margulies DS, Brauer J (2016) Longitudinal changes in resting-state fMRI from age 5 to age 6 years covary with language development. Neuroimage 128:116–124. https://doi.org/10.1016/J.NEUROIMAGE.2015.12.008 Dehaene-Lambertz G, Dehaene S, Hertz-Pannier L (2002) Functional neuroimaging of speech perception in infants. Science 298:2013–2015. https://doi.org/10.1126/science.1077066 Redcay E, Haist F, Courchesne E (2008) Functional neuroimaging of speech perception during a pivotal period in language acquisition. Dev Sci 11:237–252. https://doi.org/10.1111/j.1467-7687.2008.00674.x Duff MC, Brown-Schmidt S (2012) The hippocampus and the flexible use and processing of language. Front Hum Neurosci 6:1–11. https://doi.org/10.3389/fnhum.2012.00069 Piai V, Anderson KL, Lin JJ, et al (2016) Direct brain recordings reveal hippocampal rhythm underpinnings of language processing. Proc Natl Acad Sci U S A 113:11366–11371. https://doi.org/10.1073/pnas.1603312113 MacKay DG, Stewart R, Burke DM (1998) H.M. revisited: relations between language comprehension, memory, and the hippocampal system. J Cogn Neurosci 10:377–394. https://doi.org/10.1162/089892998562807 MacKay DG, Burke DM, Stewart R (1998) H.M.’s Language Production Deficits: Implications for Relations between Memory, Semantic Binding, and the Hippocampal System. J Mem Lang 38:28–69. https://doi.org/10.1006/JMLA.1997.2544 Klooster NB, Duff MC (2015) Remote semantic memory is impoverished in hippocampal amnesia. Neuropsychologia 79:42–52. https://doi.org/10.1016/J.NEUROPSYCHOLOGIA.2015.10.017 Banker SM, Gu X, Schiller D, Foss-Feig JH (2021) Hippocampal contributions to social and cognitive deficits in autism spectrum disorder. Trends Neurosci 44:793. https://doi.org/10.1016/J.TINS.2021.08.005 Long J, Li H, Liu Y, et al (2024) Insights into the structure and function of the hippocampus: implications for the pathophysiology and treatment of autism spectrum disorder. Front Psychiatry 15:1364858. https://doi.org/10.3389/FPSYT.2024.1364858 Lee JK, Nordahl CW, Amaral DG, et al (2015) Assessing hippocampal development and language in early childhood: Evidence from a new application of the Automatic Segmentation Adapter Tool. Hum Brain Mapp 36:4483–4496. https://doi.org/10.1002/HBM.22931 Price C (2010) The anatomy of language: a review of 100 fMRI studies published in 2009. Ann N Y Acad Sci 1191:62–88. https://doi.org/10.1111/J.1749-6632.2010.05444.X Binder JR, Desai RH, Graves WW, Conant LL (2009) Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cereb cortex 19:2767–2796. https://doi.org/10.1093/CERCOR/BHP055 Zoccante L, Viviani A, Ferro A, et al (2010) Increased left parietal volumes relate to delayed language development in autism: a structural MRI study. Funct Neurol 25:217–221 Hyde KL, Samson F, Evans AC, Mottron L (2010) Neuroanatomical differences in brain areas implicated in perceptual and other core features of autism revealed by cortical thickness analysis and voxel-based morphometry. Hum Brain Mapp 31:556–566. https://doi.org/10.1002/HBM.20887 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 08 Nov, 2024 Read the published version in European Child & Adolescent Psychiatry → Version 1 posted Editorial decision: Revision requested 06 Sep, 2024 Reviews received at journal 05 Sep, 2024 Reviews received at journal 06 Aug, 2024 Reviewers agreed at journal 06 Aug, 2024 Reviewers agreed at journal 30 Jul, 2024 Reviewers invited by journal 23 Jul, 2024 Editor assigned by journal 03 Jul, 2024 Submission checks completed at journal 03 Jul, 2024 First submitted to journal 02 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4673621","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":330858602,"identity":"fc2cebb6-3194-4f70-96a9-9b452664d7cf","order_by":0,"name":"Yaqiong Xiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYNACAwYGfhCdUECKFskGkBYDkiw6ANVLWOXxwxuYeQrs5IzPr0788MCAQZ5f7AABLWfSChhnGCQbm914u1kC6DDDmbMTCLknx4DhgwFz4rYbZzeAtCQY3Cak5fwbkLL6xM0zzm7+QZyWG2BbDidu4O/dRpwtkjeeFRycYXDcWOIG7zaLBAMJwn7hO5+88THPn2o5/v6zm2/+qLCR55cmoEXhADRGGCTAKiXwKwcB+QZY9PEfIKx6FIyCUTAKRiYAAOEcRaFnyec0AAAAAElFTkSuQmCC","orcid":"","institution":"Shenzhen Institute of Neuroscience","correspondingAuthor":true,"prefix":"","firstName":"Yaqiong","middleName":"","lastName":"Xiao","suffix":""},{"id":330858603,"identity":"e443db3b-dc83-41c3-b5a3-7baa365a5328","order_by":1,"name":"Ningxuan Zhang","email":"","orcid":"","institution":"Shenzhen Institute of Neuroscience","correspondingAuthor":false,"prefix":"","firstName":"Ningxuan","middleName":"","lastName":"Zhang","suffix":""},{"id":330858604,"identity":"f1bf57f6-fea7-4adb-a94d-7ab8ef3ebe0a","order_by":2,"name":"Shuiqun Zhang","email":"","orcid":"","institution":"The Third Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuiqun","middleName":"","lastName":"Zhang","suffix":""},{"id":330858605,"identity":"55ca9433-e325-4760-afda-48f1659d0858","order_by":3,"name":"Kaiyu Huang","email":"","orcid":"","institution":"Shenzhen Institute of Neuroscience","correspondingAuthor":false,"prefix":"","firstName":"Kaiyu","middleName":"","lastName":"Huang","suffix":""},{"id":330858606,"identity":"efea6cc6-6f5c-4257-a20c-4b5cca0c1ad1","order_by":4,"name":"Jin Xin","email":"","orcid":"","institution":"Foshan Clinical Medical School of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Xin","suffix":""},{"id":330858607,"identity":"09bad827-9d79-4f78-af0d-e6c42654313c","order_by":5,"name":"Qishan Huang","email":"","orcid":"","institution":"Foshan Clinical Medical School of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qishan","middleName":"","lastName":"Huang","suffix":""},{"id":330858608,"identity":"249dc5e1-1936-449b-a17e-5761c105d4d5","order_by":6,"name":"Aiwen Yi","email":"","orcid":"","institution":"The Third Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Aiwen","middleName":"","lastName":"Yi","suffix":""}],"badges":[],"createdAt":"2024-07-02 10:47:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4673621/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4673621/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00787-024-02605-5","type":"published","date":"2024-11-08T15:57:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61183684,"identity":"96cd5cd0-74b4-4b14-aa61-cba420be36ae","added_by":"auto","created_at":"2024-07-26 17:07:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":212046,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant differences between TD and two ASD subgroups in various developmental domains including gross motor (A), fine motor (B), language (C), personal-social (D), adaptive behavior (E), and total scores (F). ** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003e p\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4673621/v1/7c658ab279827fb774045ddd.png"},{"id":61182742,"identity":"bd1e55c1-903e-489a-8122-dc57f6dc4627","added_by":"auto","created_at":"2024-07-26 16:59:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":246189,"visible":true,"origin":"","legend":"\u003cp\u003eGroup differences in grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF), and total intracranial volume between TD and two ASD subgroups. Significant differences were observed in volume of GM (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and WM (\u003cem\u003ep\u003c/em\u003e = 0.001), but not in total volume of CSF or TIV after the FDR correction. Cohen’s \u003cem\u003ed\u003c/em\u003e, standardized effect sizes for group comparison. \u003csup\u003e*** \u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001,\u003csup\u003e **\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4673621/v1/c8f9a7568bc2c286f9189908.png"},{"id":61182744,"identity":"2b704441-8b5d-44eb-a2b8-a456a6867f5a","added_by":"auto","created_at":"2024-07-26 16:59:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":284155,"visible":true,"origin":"","legend":"\u003cp\u003eClusters showing significant correlations between grey matter (GM) volume and language scores in the ASD\u003csub\u003emoderate\u003c/sub\u003e subgroup (A). Scatter plots demonstrate significant relationships of language scores with grey matter volume in the left STG/ITG (B), left hippocampus (C), left IPL (D), right STG (E), right hippocampus (F), and bilateral precuneus (G). The \u003cem\u003er\u003c/em\u003e values are calculated from the partial correlations between grey matter volume and language scores, controlling for age, gender, and TIV. STG, superior temporal gyrus; ITG, inferior temporal gyrus; IPL, inferior parietal lobe.\u003csup\u003e *** \u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001,\u003csup\u003e **\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4673621/v1/2373159a1ae35c734b05d0e9.png"},{"id":68749822,"identity":"28a4c29b-e3d1-41e3-8785-a74ffd55c915","added_by":"auto","created_at":"2024-11-11 16:05:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1250991,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4673621/v1/ead1de53-987e-4659-b168-49dbd9d8e9cf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Neuroanatomical Basis of Language Ability in an Autism Subgroup with Moderate Language Deficits","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIndividuals with autism spectrum disorder (ASD) exhibit significant heterogeneity in their language abilities. Some individuals with ASD demonstrate language skills that are nearly within the typical range or minor language impairments, while a considerable proportion of individuals with ASD remain minimally verbal, experiencing profound challenges in expressive and receptive language abilities [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This wide spectrum of language functioning within the ASD population underscores the complexity of understanding language deficits in this disorder.\u003c/p\u003e \u003cp\u003eFunctional magnetic resonance imaging (fMRI) studies have provided insights into the neural underpinnings of language impairments in ASD, such as abnormal inter-hemispheric functional connectivity between language-related regions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], reduced neural activation in language regions [\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and aberrant connectivity patterns between temporal cortex and visual region [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Collectively, these findings demonstrate the multifaceted nature of language impairments in ASD and highlight the importance of understanding the complex interplay between neural circuitry and language function in this population.\u003c/p\u003e \u003cp\u003eIn recent years, investigations into neuroanatomical alterations associated with ASD have consistently reported structural abnormalities in young children with ASD as compared to typically developing (TD) controls [\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These studies underscore the presence of distinct neuroanatomical differences associated with ASD pathology. Moreover, some studies have shown the behavioral relevance of these ASD-related structural alterations in young children [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], further emphasizing the significance of understanding the neural underpinnings of ASD symptoms.\u003c/p\u003e \u003cp\u003eA few studies have also demonstrated the relationships between brain morphological features and language ability in school-age children with ASD [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and in preschool children with ASD [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. For example, a recent study reported grey matter thickness and gyrification of language‑related areas were related to language abilities in children with ASD [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Arutiunian et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] demonstrated diminished grey matter volume in the bilateral amygdala and hippocampus among school-age children with ASD. Joseph et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] found less left-lateralized asymmetry in language-related regions, and the reduced leftward asymmetry of language region (i.e., pars opercularis) was correlated with better language abilities in children with ASD. The study by Naigles et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] divided a group of preschool boys with ASD into three subgroups based on their language abilities and reported that the subgroups differed in the bilateral inferior longitudinal fasciculus. Additionally, they found that the fractional anisotropy along this fiber tract significantly correlated with language scores across the ASD subgroups [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Our recent study reported associations between grey matter volume of prefrontal cortex and cerebellum and language and social abilities in children without ASD, yet these associations were notably absent in children with ASD [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Despite these explorations, the underlying neuroanatomical foundation of early language deficits in ASD remains largely elusive, which leads to challenges in understanding the heterogeneity of language functions in ASD and its underlying mechanisms.\u003c/p\u003e \u003cp\u003eIn the present study, we aimed to investigate the neuroanatomical basis related to language deficits in children with ASD and varying language deficits. We collected structural MRI data from a relatively large cohort (69 ASD/38 TD) of young Chinese children aged 1.5\u0026ndash;6.5 years. Within the ASD group, children were divided into two subgroups based on the median of their language scores. First, we compared global brain volumes and regional grey matter volume between TD controls and two ASD subgroups to identify any significant group differences. Our hypothesis posited that ASD subgroups would exhibit greater grey matter volume compared to the TD group. Subsequently, we investigated the correlations between language scores and grey matter volume within the TD group and two ASD subgroups separately. We anticipated distinct correlation patterns within the ASD subgroups reflecting their varying language deficits. Given previous findings demonstrated associations between language scores and grey matter volume in non-ASD children [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], we anticipated there would be similar correlation patterns in the TD group.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003e In this study, we recruited a total of 107 participants (69 ASD/38 TD) aged 1.5 to 6.5 years from the Foshan Fosun Chancheng Hospital, Foshan, China between November 2021 and May 2023. All participants completed the Gesell Development Schedule (GDS), which measures various developmental domains including fine motor, gross motor, personal-social, language, and adaptive behavior [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The parents or guardians of all participants completed the Autism Behavior Checklist (ABC), a widely used questionnaire for assessing autistic behaviors and symptom severity in individuals with ASD [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Although the ABC was not utilized for diagnosing ASD, it is notable that all TD children had an ABC score\u0026thinsp;\u0026lt;\u0026thinsp;44, indicating the absence of ASD. Conversely, children with ASD had ABC scores\u0026thinsp;\u0026ge;\u0026thinsp;53. All participants diagnosed with ASD met the DSM-V criteria for ASD through clinical interview and underwent either the Autism Diagnostic Observation Schedule (ADOS Module 1 or 2) or the Childhood Autism Rating Scale (CARS), administered by the same clinician. TD participants had a total score\u0026thinsp;\u0026gt;\u0026thinsp;85 on the GDS, indicating normal development. All children were native Mandarin or Cantonese speakers with normal hearing and no family history of mental or psychiatric disorders. This study was approved by the Foshan Fosun Chancheng Hospital. Informed consent was obtained from parents or guardians of all participants.\u003c/p\u003e \u003cp\u003eTwo participants were excluded from the analysis due to poor MRI data quality (1 ASD/1 TD). Given the diverse range of language abilities observed within the remaining ASD sample, we stratified the ASD participants into two subgroups based on the median of their language scores obtained from the GDS. Specifically, ASD participants were classified as ASD with moderate language deficits (ASD\u003csub\u003emoderate\u003c/sub\u003e, n\u0026thinsp;=\u0026thinsp;34) if they had a language score\u0026thinsp;\u0026ge;\u0026thinsp;44.4, while those with a language score\u0026thinsp;\u0026lt;\u0026thinsp;44.4 were categorized as ASD with severe language deficits (ASD\u003csub\u003esevere\u003c/sub\u003e, n\u0026thinsp;=\u0026thinsp;34). The detailed demographic, clinical, and behavioral information of TD group and two ASD subgroups is summarized in Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;1.\u003c/b\u003e Demographic details and clinical and behavioral testing scores.\u003c/p\u003e \u003c/div\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.30116472545757%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"26.289517470881865%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTD\u003c/p\u003e\n \u003cp\u003e(n = 37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.9567387687188%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eASD\u003csub\u003emoderate\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e(n = 34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.452579034941763%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eASD\u003csub\u003esevere\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e(n = 34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.30116472545757%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\" valign=\"top\"\u003e\n \u003cp\u003eMean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.81198003327787%\" valign=\"top\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\" valign=\"top\"\u003e\n \u003cp\u003eMean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\" valign=\"top\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.973377703826955%\" valign=\"top\"\u003e\n \u003cp\u003eMean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\" valign=\"top\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.30116472545757%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.39\u0026plusmn;1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.81198003327787%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.5-5.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.31\u0026plusmn;1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.52-6.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.973377703826955%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.039\u0026plusmn;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\" valign=\"bottom\"\u003e\n \u003cp\u003e2-5.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.30116472545757%\"\u003e\n \u003cp\u003eGender (M/F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.289517470881865%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e32/5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.9567387687188%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e32/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.452579034941763%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e25/9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003eGesell subscale scores\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.30116472545757%\"\u003e\n \u003cp\u003eGross motor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e97.42\u0026plusmn;6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.81198003327787%\"\u003e\n \u003cp\u003e81.1-107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e77.76\u0026plusmn;8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\"\u003e\n \u003cp\u003e47.8-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.973377703826955%\" valign=\"bottom\"\u003e\n \u003cp\u003e71.79\u0026plusmn;8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\" valign=\"bottom\"\u003e\n \u003cp\u003e52-87.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.30116472545757%\"\u003e\n \u003cp\u003eFine motor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e96.73\u0026plusmn;5.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.81198003327787%\"\u003e\n \u003cp\u003e86-108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e73.31\u0026plusmn;9.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\"\u003e\n \u003cp\u003e54.1-90.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.973377703826955%\" valign=\"bottom\"\u003e\n \u003cp\u003e64.25\u0026plusmn;10.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\" valign=\"bottom\"\u003e\n \u003cp\u003e45.9-84.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.30116472545757%\"\u003e\n \u003cp\u003ePersonal-social\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e93.76\u0026plusmn;4.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.81198003327787%\"\u003e\n \u003cp\u003e80-103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e60.46\u0026plusmn;7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\"\u003e\n \u003cp\u003e31-70.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.973377703826955%\" valign=\"bottom\"\u003e\n \u003cp\u003e48.16\u0026plusmn;7.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\" valign=\"bottom\"\u003e\n \u003cp\u003e30.2-64.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.30116472545757%\"\u003e\n \u003cp\u003eLanguage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e91.47\u0026plusmn;5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.81198003327787%\"\u003e\n \u003cp\u003e75.7-101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e55.07\u0026plusmn;7.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\"\u003e\n \u003cp\u003e44.6-70.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.973377703826955%\" valign=\"bottom\"\u003e\n \u003cp\u003e37.11\u0026plusmn;5.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\" valign=\"bottom\"\u003e\n \u003cp\u003e23.6-44.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.30116472545757%\"\u003e\n \u003cp\u003eAdaptive\u003csup\u003e\u0026nbsp;\u003c/sup\u003ebehavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e93.79\u0026plusmn;5.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.81198003327787%\"\u003e\n \u003cp\u003e80-107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e66.18\u0026plusmn;8.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\"\u003e\n \u003cp\u003e40.9-82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.973377703826955%\"\u003e\n \u003cp\u003e55.07\u0026plusmn;13.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\"\u003e\n \u003cp\u003e31.6-93.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.30116472545757%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e94.34\u0026plusmn;4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.81198003327787%\"\u003e\n \u003cp\u003e85.1-100.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e66.72\u0026plusmn;5.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\"\u003e\n \u003cp\u003e49.1-79.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.973377703826955%\"\u003e\n \u003cp\u003e55.25\u0026plusmn;7.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\"\u003e\n \u003cp\u003e39.3-68.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.30116472545757%\"\u003e\n \u003cp\u003eABC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e25.3\u0026plusmn;8.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.81198003327787%\"\u003e\n \u003cp\u003e8-43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e66.65\u0026plusmn;14.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\"\u003e\n \u003cp\u003e53-107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.973377703826955%\"\u003e\n \u003cp\u003e74.97\u0026plusmn;19.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\"\u003e\n \u003cp\u003e54-130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003eADOS scores\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.30116472545757%\"\u003e\n \u003cp\u003eADOS SA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.81198003327787%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e12.41\u0026plusmn;3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\"\u003e\n \u003cp\u003e7-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.973377703826955%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.04\u0026plusmn;3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\" valign=\"bottom\"\u003e\n \u003cp\u003e8-22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.30116472545757%\"\u003e\n \u003cp\u003eADOS RRB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.81198003327787%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e1.24\u0026plusmn;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\"\u003e\n \u003cp\u003e0-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.973377703826955%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.52\u0026plusmn;1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\" valign=\"bottom\"\u003e\n \u003cp\u003e0-4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.30116472545757%\"\u003e\n \u003cp\u003eADOS Total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.81198003327787%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e13.66\u0026plusmn;4.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\"\u003e\n \u003cp\u003e8-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.973377703826955%\" valign=\"bottom\"\u003e\n \u003cp\u003e14.56\u0026plusmn;3.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\" valign=\"bottom\"\u003e\n \u003cp\u003e9-24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.30116472545757%\"\u003e\n \u003cp\u003eCARS Total\u003csup\u003e##\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e14.33\u0026plusmn;1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.81198003327787%\"\u003e\n \u003cp\u003e13-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.477537437603994%\"\u003e\n \u003cp\u003e31.92\u0026plusmn;2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\"\u003e\n \u003cp\u003e30-38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.973377703826955%\" valign=\"bottom\"\u003e\n \u003cp\u003e35\u0026plusmn;4.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.479201331114808%\" valign=\"bottom\"\u003e\n \u003cp\u003e25-45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: ABC, Autism, Behavior Checklist; RRB, Restricted and Repetitive Behavior; ADOS, Autism Diagnostic Observation Schedule; CARS,\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e#\u0026nbsp;\u003c/sup\u003eADOS was administrated in 54 ASD children (ASD\u003csub\u003emoderate\u003c/sub\u003e: n = 29; ASD\u003csub\u003esevere\u003c/sub\u003e: n = 25).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e##\u0026nbsp;\u003c/sup\u003eCARS was administrated in 8 TD children and 60 ASD children (ASD\u003csub\u003emoderate\u003c/sub\u003e: n = 26; ASD\u003csub\u003esevere\u003c/sub\u003e: n = 34).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMRI data acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore MRI scanning, all of the participants were administered 0.5% chloral hydrate 0.5 ml/kg (maximum dose 10 ml) orally to induce and maintain sleep. All participants continued sleeping during scanning. All the structural MRI data were collected on a 3.0T SEMENS Skyra at the Foshan Chancheng Hospital, Foshan, China using a T1-weighted MPRAGE sequence (TE = 2.98 ms, TR = 2300 ms, resolution = 1.0 x 1.0 x 1.0 mm\u003csup\u003e3\u003c/sup\u003e, space gap=0, slice thickness = 1 mm, flip angle =9\u0026deg;, 144 slices, a total of 5 min 9 s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImaging data preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to preprocessing, MR images were visually inspected and then normalized to standard AC/PC orientations. To extract grey matter maps, MRI data were processed with the Voxel-based morphometry (VBM) pipeline using the Computational Anatomy Toolbox (CAT 12; https://neuro-jena.github.io/cat/) for Statistical Parametric Mapping (SPM12; http://www.fil.ion.ucl.ac.uk), running in Matlab R2020a (MathWorks, Natick, MA, USA). Here, to minimize the potential confounds introduced by the different brain sizes and tissues between young children and adults [28], customized pediatric tissue probability maps and the diffeomorphic anatomical registration through exponentiated lie algebra (DARTEL) templates were created with the Cerebromatic (COM) Toolbox [29, 30] which provides regression parameters modeled with 1914 healthy participants aged 13 months to 75 years. Specifically, the COM toolbox can be used to generate the custom tissue probability maps that matches sample demographics to parameters that influence brain structure using a flexible non-parametric approach: multivariate adaptive regression splines [29]. The custom DARTEL templates can also be created using the COM toolbox, which matches sample demographics to a second set of regression parameters derived from 1919 participants in the same databases [30]. Here, the age and sex of each participant, and the field strength were entered into the COM toolbox to create the custom tissue probability maps and DARTEL templates, separately.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the VBM analysis, MRI images were segmented into grey matter, white matter, and cerebrospinal fluid (CSF). Following segmentation, the grey matter images were affine registered to pediatric tissue probability maps previously generated, and then they were spatially normalized to a study-specific pediatric template using DARTEL registration. Subsequently, the grey matter images were modulated with Jacobian determinants from the normalization process to preserve regional volumes. Quality control measures were implemented to ensure sample homogeneity, with no outlier images identified. The grey matter images underwent smoothing using an 8 mm full-width at half-maximum (FWHM) kernel. Processed grey matter images had a voxel size of 1.5 mm \u0026times;\u0026thinsp;1.5\u0026thinsp; mm \u0026times;\u0026thinsp;1.5\u0026thinsp;mm. Finally, total grey matter, white matter, CSF, and intracranial volume (TIV) measurements were extracted for each participant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGroup differences in\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003edemographic and clinical data\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses for demographic and clinical data were performed using R software (version 4.1.2). Specifically, differences between the TD group and the two ASD subgroups in demographic information (i.e., age) and behavioral testing (total and domain scores of GDS) were assessed using two-sample\u003cem\u003e\u0026nbsp;t\u003c/em\u003e-tests, while differences in gender were assessed using the Chi-square test. Comparisons of clinical (e.g., ADOS, CARS, ABC) scores were conducted only between the two ASD subgroups. Results for age and total and domain scores of GDS were corrected for multiple comparisons using the false discovery rate (FDR) approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eComparisons of global volumes between TD and two ASD subgroups\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed the group differences among the TD group and the ASD subgroups in volumes of grey matter, white matter, CSF using one-way analysis of covariance (ANCOVA), while controlling for age, gender, and TIV. Additionally, a group comparison of TIV was conducted while controlling for age and gender.\u0026nbsp;The results were corrected for multiple comparisons using the FDR correction (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). For significant group differences, post-hoc \u003cem\u003et\u003c/em\u003e-tests were conducted to further explore the differences between TD, ASD\u003csub\u003emoderate\u003c/sub\u003e, and ASD\u003csub\u003esevere\u003c/sub\u003e, with corrections for multiple testing using the FDR approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGroup differences in regional grey matter volume\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe whole brain voxel-wise analyses were performed to examine the group differences \u0026nbsp;between TD and two ASD subgroups in grey matter volume using the \u0026lsquo;y_TTest2_Image\u0026rsquo; function in DAPBI (a toolbox for Data Processing \u0026amp; Analysis for Brain Imaging; https://rfmri.org/dpabi) [31], with age, gender, and TIV as covariates. We used a binary grey matter mask for the group analysis that was created with all the grey matter images across participants. Results were corrected at the cluster-level using Gaussian random field (GRF) theory, in which the \u003cem\u003et\u003c/em\u003e map was converted to the z map (|Z| \u0026ge; 3.29, voxel-wise \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, cluster size \u0026gt; 800 voxels, cluster-wise \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, two-tailed). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCorrelations between grey matter volume and language ability\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther, we examined the correlations between whole-brain grey matter volume and language ability in TD and two ASD subgroups separately, using the \u0026lsquo;y_Correlation_Image\u0026rsquo; function in DPABI, while controlling for age, gender, and TIV. The resulting correlation maps were converted to the \u003cem\u003ez\u003c/em\u003e maps and corrected for multiple comparisons at the cluster-level using the GRF correction (|Z| \u0026ge; 3.29, voxel-wise \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, cluster size \u0026gt; 650 voxels, cluster-wise \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, two-tailed). Finally, all the significant clusters were visualized with the BrainNet Viewer (http://www.nitrc.org/ projects/bnv/).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eResults of group comparisons in demographic and clinical data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNo differences were observed at age or gender between TD and two ASD subgroups or between two ASD subgroups after correcting for multiple comparisons (\u003cem\u003ep\u003c/em\u003es \u0026gt; 0.05). As shown in Figure 1, there were significant differences in all domains measured by the GDS between TD and two ASD subgroups (\u003cem\u003ep\u003c/em\u003es \u0026lt; 0.001), and between two ASD subgroups (\u003cem\u003ep\u003c/em\u003es \u0026lt; 0.001, except for gross motor \u003cem\u003ep\u003c/em\u003e = 0.008). Significant differences were found between two ASD subgroups in scores of CARS (\u003cem\u003ep\u003c/em\u003e = 0.002) and ABC scores (\u003cem\u003ep\u003c/em\u003e = 0.038), but not in ADOS scores (\u003cem\u003ep\u003c/em\u003es \u0026gt; 0.1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSignificant differences in total brain volumes\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 2, significant differences between TD and two ASD subgroups were observed in total volumes of grey matter (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2, 99)\u003c/sub\u003e = 33.22, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and white matter (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2, 99)\u003c/sub\u003e = 6.96, \u003cem\u003ep\u003c/em\u003e = 0.001), but not in total volume of CSF (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2, 99)\u003c/sub\u003e = 0.76, \u003cem\u003ep\u003c/em\u003e = 0.44), controlling for age, gender, and TIV. There were differences between ASD\u003csub\u003emoderate\u0026nbsp;\u003c/sub\u003eand ASD\u003csub\u003esevere\u0026nbsp;\u003c/sub\u003esubgroups in TIV (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 63)\u003c/sub\u003e = 3.71, \u003cem\u003ep\u003c/em\u003e = 0.028) when controlling for age and gender. However, this result was not significant after controlling for multiple comparisons using the FDR method. Post-hoc analyses only showed significant group differences in grey matter volume between the TD group and ASD\u003csub\u003emoderate\u0026nbsp;\u003c/sub\u003esubgroup (\u003cem\u003ep\u003c/em\u003e = 0.047, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e = 0.55, 95% CI [0.07, 1.03]).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNo significant differences in regional grey matter volume between TD and two ASD subgroups\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe whole-brain voxel-wise analysis did not show significant group differences in grey matter volume between TD and two ASD subgroups after correcting for multiple comparisons with the GRF method (|Z| \u0026ge; 3.29, voxel-wise \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, cluster size \u0026gt; 800 voxels, cluster-wise \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, two-tailed).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSignificant grey matter volume\u0026ndash;language correlations in the ASD\u003csub\u003emoderate\u003c/sub\u003e subgroup\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe observed significant correlations between grey matter volume and language scores in the ASD\u003csub\u003emoderate\u003c/sub\u003e subgroup controlling for age, gender, and TIV. Specifically, there were positive associations in the left superior/inferior temporal gyrus (STG/ITG) (\u003cem\u003er\u003c/em\u003e = 0.7, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), right STG (\u003cem\u003er\u003c/em\u003e = 0.71, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), bilateral hippocampus extending to parahippocampal gyrus (left: \u003cem\u003er\u003c/em\u003e = 0.49, \u003cem\u003ep\u003c/em\u003e = 0.005; right: \u003cem\u003er\u003c/em\u003e = 0.55, \u003cem\u003ep\u003c/em\u003e = 0.0015), and left inferior parietal lobe (IPL) (\u003cem\u003er\u003c/em\u003e = 0.55, \u003cem\u003ep\u003c/em\u003e = 0.0015), and negative correlations in the bilateral precuneus (\u003cem\u003er\u003c/em\u003e = -0.55, \u003cem\u003ep\u003c/em\u003e = 0.0015) (see Figure 3 and Table 2). However, no significant correlations were observed in the ASD\u003csub\u003esevere\u0026nbsp;\u003c/sub\u003esubgroup or TD group. All the resulting clusters were corrected for multiple comparisons (|Z| \u0026ge; 3.29, voxel-wise \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, cluster size \u0026gt; 650 voxels, cluster-wise \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, two-tailed).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Clusters showed significant correlations with Gesell language scores in the ASD\u003csub\u003emoderate\u003c/sub\u003e subgroup.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"630\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.61904761904762%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.857142857142854%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeak MNI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.65079365079365%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeak Z value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.873015873015873%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVoxel size\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mm\u003csup\u003e3\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.20074349442379%\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.598513011152416%\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.20074349442379%\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.66295707472178%\"\u003e\n \u003cp\u003eLeft hippocampus, parahippocampal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.626391096979333%\"\u003e\n \u003cp\u003e-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\"\u003e\n \u003cp\u003e-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.626391096979333%\"\u003e\n \u003cp\u003e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.672496025437201%\"\u003e\n \u003cp\u003e6.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.89825119236884%\"\u003e\n \u003cp\u003e2579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.66295707472178%\"\u003e\n \u003cp\u003eLeft superior temporal gyrus, inferior temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.626391096979333%\"\u003e\n \u003cp\u003e-56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\"\u003e\n \u003cp\u003e-65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.626391096979333%\"\u003e\n \u003cp\u003e-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.672496025437201%\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.89825119236884%\"\u003e\n \u003cp\u003e2453\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.66295707472178%\"\u003e\n \u003cp\u003eRight superior temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.626391096979333%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\"\u003e\n \u003cp\u003e-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.626391096979333%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.672496025437201%\"\u003e\n \u003cp\u003e5.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.89825119236884%\"\u003e\n \u003cp\u003e1058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.66295707472178%\"\u003e\n \u003cp\u003eRight hippocampus, parahippocampal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.626391096979333%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\"\u003e\n \u003cp\u003e-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.626391096979333%\"\u003e\n \u003cp\u003e-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.672496025437201%\"\u003e\n \u003cp\u003e5.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.89825119236884%\"\u003e\n \u003cp\u003e874\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.66295707472178%\"\u003e\n \u003cp\u003eLeft inferior parietal lobe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.626391096979333%\"\u003e\n \u003cp\u003e-48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\"\u003e\n \u003cp\u003e-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.626391096979333%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.672496025437201%\"\u003e\n \u003cp\u003e4.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.89825119236884%\"\u003e\n \u003cp\u003e689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.66295707472178%\"\u003e\n \u003cp\u003eBilateral precuneus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.626391096979333%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\"\u003e\n \u003cp\u003e-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.626391096979333%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.672496025437201%\"\u003e\n \u003cp\u003e-4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.89825119236884%\"\u003e\n \u003cp\u003e844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we stratified ASD children into moderate and severe language deficits subgroups to capture the heterogeneity in language ability among the ASD sample. We observed significant group differences in grey matter volume, with post-hoc analyses specifically indicating significant disparities between the TD group and ASD\u003csub\u003emoderate\u003c/sub\u003e subgroup. Moreover, significant correlations between grey matter volume and language scores were observed exclusively within the ASD\u003csub\u003emoderate\u003c/sub\u003e subgroup, included positive associations in the bilateral STG, hippocampus, and left IPL, and negative correlations in the bilateral precuneus. These findings suggest potential neural underpinnings related to the specific neurodevelopmental profile of individuals with moderate language deficits in ASD.\u003c/p\u003e\n\u003cp\u003eNotably, we observed significantly positive correlations between grey matter volume in the bilateral STG and language scores in the ASD\u003csub\u003emoderate\u003c/sub\u003e subgroup. This finding aligns with previous research highlighting the role of the STG in language processing\u0026nbsp;[32\u0026ndash;34]\u0026nbsp;and its structural abnormalities in individuals with ASD\u0026nbsp;[13, 35, 36]. The STG is a critical region for language comprehension and processing and has been extensively studied in the context of typical language development\u0026nbsp;[37\u0026ndash;39]. Moreover, a number of fMRI studies have consistently demonstrated reduced activation\u0026nbsp;of the bilateral STG during speech processing in young children with ASD\u0026nbsp;[6\u0026ndash;10]. While\u0026nbsp;research has reported reduced grey matter of\u0026nbsp;the STG in individuals with ASD compared to controls\u0026nbsp;[35, 36], associations with language deficits have not been documented. Our findings extend prior observations by demonstrating that within a specific subgroup of ASD individuals with moderate language deficits, there is a clear relationship between grey matter volume of the STG and language abilities. This suggests that the degree of grey matter reduction in the STG may serve as a biomarker for the severity of language impairment in ASD with moderate language deficits.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, we found a significant positive correlation between grey matter volume in the bilateral hippocampus and language scores in the ASD\u003csub\u003emoderate\u003c/sub\u003e subgroup. The hippocampus is traditionally associated with memory and spatial navigation; however, recent studies have highlighted its involvement in language processing and development\u0026nbsp;[40\u0026ndash;44]. Notably, recent research has demonstrated reduced grey matter volume in the hippocampus and its associations with language deficits in children with ASD\u0026nbsp;[21, 45]. Our findings provide further evidence for these associations, suggesting that in children with ASD\u0026mdash;particularly those with moderate language deficits\u0026mdash;the hippocampus may play a crucial compensatory role in supporting language function. The observed correlations may reflect the hippocampus\u0026apos;s role in facilitating language learning and memory retrieval processes, which are often impaired in ASD\u0026nbsp;[45, 46]. Previous neuroimaging studies have shown reduced hippocampal volumes in individuals with ASD, which correlates with the severity of cognitive and language deficits\u0026nbsp;[23, 47]. Our results add to this body of work by indicating that within a subgroup of children with moderate language deficits, those with greater hippocampal grey matter volume exhibit better language abilities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, our data demonstrated significant associations between grey matter volume in the left IPL and language scores in the ASD\u003csub\u003emoderate\u003c/sub\u003e subgroup. The IPL plays a critical role in language processing, as it is involved in phonological processing and the integration of sensory information necessary for language comprehension and production\u0026nbsp;[48]. It has also been implicated in various aspects of language function, including syntactic processing and semantic integration\u0026nbsp;[32, 49]. Research has shown that grey matter volume in the left parietal lobe is associated with delayed language development in children with ASD\u0026nbsp;[50]. Research found that children with ASD exhibit increased grey matter volume in language-related regions, including the left parietal lobe\u0026nbsp;[51]. This increase could reflect compensatory mechanisms or neural plasticity as the brain attempts to adapt to and mitigate language difficulties in individuals with ASD. Such neural plasticity might be particularly pronounced in children with moderate language deficits, suggesting that their brains are actively reorganizing to support language functions despite the challenges posed by ASD.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite the presence of significant correlations between grey matter volume and language abilities in ASD children with moderate language deficits, such correlations were absent in ASD children with severe language deficits and TD children. This suggests that the relationship between grey matter volume and language abilities varies across different stages of development. ASD children with moderate language deficits might be at a developmental stage where grey matter alterations have a more pronounced impact on language skills. In contrast, ASD children with severe language deficits and TD children may follow different developmental trajectories regarding brain-behavior relationships. This finding highlights heterogeneity within the ASD population. ASD children with moderate language deficits may represent a subgroup with specific neuroanatomical characteristics that predispose them to language impairments. Conversely, ASD children with severe language deficits may have different underlying neurobiological mechanisms contributing to their language difficulties, which may not be captured by grey matter volume alone. It is also possible that the absence of the brain-behavior relationships in other groups may be due to relatively smaller individual variability within the TD group (SD = 5.55) and the ASD\u003csub\u003esevere\u003c/sub\u003e subgroup (SD = 5.996) compared to the ASD\u003csub\u003emoderate\u003c/sub\u003e subgroup (SD = 7.76).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA few limitations of the present study should be noted. First, the cross-sectional design limits our ability to infer causality between grey matter volume and language abilities. Longitudinal studies are needed to determine whether changes in brain structure over time are directly associated with changes in language skills, particularly in identifying how early differences in grey matter volume might predict future language development. Second, the sample size here was relatively small, which may affect the generalizability of the findings. Future research should include larger cohorts to validate these results and provide more robust conclusions. Third, the stratification of subgroups may be biased by the sample included in this study, as the heterogeneity of language abilities within the ASD population could have varied effects on subgroup classifications. Finally, while this study classified ASD children into moderate and severe language deficit subgroups, further stratification or additional subgroups could provide a more nuanced understanding of the neuroanatomical variations associated with different levels of language impairment in ASD.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the significant heterogeneity in language abilities among children with ASD and underscores the complex neuroanatomical foundations of early language deficits. By examining a cohort of Chinese children with ASD and TD, we identified distinct differences in grey matter and white matter volumes, particularly in grey matter volume between the TD group and the ASD\u003csub\u003emoderate\u003c/sub\u003e subgroup. Notably, we found significant correlations between grey matter volume and language scores exclusively within the ASD\u003csub\u003emoderate\u003c/sub\u003e subgroup. These correlations included positive associations in regions such as the bilateral STG, hippocampus, and left IPL, and negative correlations in the bilateral precuneus. Our findings provide novel evidence linking specific brain regions to language abilities in ASD children with moderate language deficits, offering new insights into the neuroanatomical basis of language deficits and contributing to a better understanding of the heterogeneity observed in ASD. This research underscores the importance of considering subgroup variations in understanding the complexity of the condition and highlights the need to account for subgroup-specific neuroanatomical characteristics when developing targeted interventions for language deficits in children with ASD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the parents and children who participated in our research; without them, this work would not be possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.X. and A.Y. designed the study. A.Y., S.Z., J.X., and Q.H. recruited the participants and collected the data. Y.X., N.Z., and K.H. analysed the data. Y.X. drafted the main manuscript. All authors reviewed the manuscript and contributed to manuscript editing and revision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (32200808), Research Capability Improvement Project of Guangzhou Medical University (2024150), and Research Fund Project of Guangdong Provincial Bureau of Traditional Chinese Medicine (20221372).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe tidy data used for the analyses reported in this article are available at https://github.com/Yaqiongxiao/asdGM.subgroups.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTager-Flusberg H, Kasari C (2013) Minimally verbal school-aged children with autism spectrum disorder: The neglected end of the spectrum. Autism Res 6:468\u0026ndash;478. https://doi.org/10.1002/aur.1329\u003c/li\u003e\n\u003cli\u003eAnderson DK, Lord C, Risi S, et al (2007) Patterns of Growth in Verbal Abilities Among Children With Autism Spectrum Disorder. J Consult Clin Psychol 75:594\u0026ndash;604. https://doi.org/10.1037/0022-006X.75.4.594\u003c/li\u003e\n\u003cli\u003eKjelgaard MM, Tager-Flusberg H (2001) An investigation of language impairment in autism: Implications for genetic subgroups. Lang Cogn Process 16:287\u0026ndash;308. https://doi.org/10.1080/01690960042000058\u003c/li\u003e\n\u003cli\u003ePickles A, Anderson DK, Lord C (2014) Heterogeneity and plasticity in the development of language: A 17-year follow-up of children referred early for possible autism. J Child Psychol Psychiatry Allied Discip 55:1354\u0026ndash;1362. https://doi.org/10.1111/jcpp.12269\u003c/li\u003e\n\u003cli\u003eDinstein I, Pierce K, Eyler L, et al (2011) Disrupted Neural Synchronization in Toddlers with Autism. Neuron 70:1218\u0026ndash;1225. https://doi.org/10.1016/J.NEURON.2011.04.018\u003c/li\u003e\n\u003cli\u003eXiao Y, Wen TH, Kupis L, et al (2022) Neural responses to affective speech, including motherese, map onto clinical and social eye tracking profiles in toddlers with ASD. Nat Hum Behav 2022 6:443\u0026ndash;454. https://doi.org/10.1038/s41562-021-01237-y\u003c/li\u003e\n\u003cli\u003eLombardo M V., Pierce K, Eyler LT, et al (2015) Different functional neural substrates for good and poor language outcome in autism. Neuron 86:267\u0026ndash;277. https://doi.org/10.1016/j.neuron.2015.03.023\u003c/li\u003e\n\u003cli\u003eLombardo M V, Pramparo T, Gazestani V, et al (2018) Large-scale associations between the leukocyte transcriptome and BOLD responses to speech differ in autism early language outcome subtypes. Nat Neurosci 21:1680\u0026ndash;1688. https://doi.org/10.1038/s41593-018-0281-3\u003c/li\u003e\n\u003cli\u003eEyler LT, Pierce K, Courchesne E, et al (2012) A failure of left temporal cortex to specialize for language is an early emerging and fundamental property of autism. Brain 135:949\u0026ndash;960. https://doi.org/10.1093/brain/awr364\u003c/li\u003e\n\u003cli\u003eRedcay E, Courchesne E (2008) Deviant Functional Magnetic Resonance Imaging Patterns of Brain Activity to Speech in 2\u0026ndash;3-Year-Old Children with Autism Spectrum Disorder. Biol Psychiatry 64:589\u0026ndash;598. https://doi.org/10.1016/J.BIOPSYCH.2008.05.020\u003c/li\u003e\n\u003cli\u003eGao Y, Linke A, Jao Keehn RJ, et al (2019) The language network in autism: Atypical functional connectivity with default mode and visual regions. Autism Res 12:1344\u0026ndash;1355. https://doi.org/10.1002/aur.2171\u003c/li\u003e\n\u003cli\u003eXiao Y, Wen TH, Kupis L, et al (2023) Atypical functional connectivity of temporal cortex with precuneus and visual regions may be an early-age signature of ASD. Mol Autism 14:1\u0026ndash;11. https://doi.org/10.1186/s13229-023-00543-8\u003c/li\u003e\n\u003cli\u003eRetico A, Giuliano A, Tancredi R, et al (2016) The effect of gender on the neuroanatomy of children with autism spectrum disorders: A support vector machine case-control study. Mol Autism 7:1\u0026ndash;20. https://doi.org/10.1186/s13229-015-0067-3\u003c/li\u003e\n\u003cli\u003eWang J, Fu K, Chen L, et al (2017) Increased gray matter volume and resting-state functional connectivity in somatosensory cortex and their relationship with autistic symptoms in young boys with autism spectrum disorder. Front Physiol 8:588. https://doi.org/10.3389/FPHYS.2017.00588/XML/NLM\u003c/li\u003e\n\u003cli\u003eCai J, Hu X, Guo K, et al (2018) Increased Left Inferior Temporal Gyrus Was Found in Both Low Function Autism and High Function Autism. Front Psychiatry 9:542. https://doi.org/10.3389/FPSYT.2018.00542/BIBTEX\u003c/li\u003e\n\u003cli\u003eXin J, Huang K, Yi A, et al (2023) Absence of associations with prefrontal cortex and cerebellum may link to early language and social deficits in preschool children with ASD. Front Psychiatry 14:1144993. https://doi.org/10.3389/fpsyt.2023.1144993\u003c/li\u003e\n\u003cli\u003eLucibello S, Verdolotti T, Giordano FM, et al (2019) Brain morphometry of preschool age children affected by autism spectrum disorder: Correlation with clinical findings. Clin Anat 32:143\u0026ndash;150. https://doi.org/10.1002/CA.23252\u003c/li\u003e\n\u003cli\u003eJoseph RM, Fricker Z, Fenoglio A, et al (2014) Structural asymmetries of language-related gray and white matter and their relationship to language function in young children with ASD. Brain Imaging Behav 8:60\u0026ndash;72. https://doi.org/10.1007/s11682-013-9245-0\u003c/li\u003e\n\u003cli\u003eRaznahan A, Lenroot R, Thurm A, et al (2013) Mapping cortical anatomy in preschool aged children with autism using surface-based morphometry. NeuroImage Clin 2:111\u0026ndash;119. https://doi.org/10.1016/j.nicl.2012.10.005\u003c/li\u003e\n\u003cli\u003eLucibello S, Bert\u0026egrave; G, Verdolotti T, et al (2021) Cortical Thickness and Clinical Findings in Prescholar Children With Autism Spectrum Disorder. Front Neurosci 15:776860. https://doi.org/10.3389/FNINS.2021.776860\u003c/li\u003e\n\u003cli\u003eArutiunian V, Gomozova M, Minnigulova A, et al (2023) Structural brain abnormalities and their association with language impairment in school-aged children with Autism Spectrum Disorder. Sci Rep 13:1172. https://doi.org/10.1038/S41598-023-28463-W/FIGURES/8\u003c/li\u003e\n\u003cli\u003eSharda M, Foster NEV, Tryfon A, et al (2017) Language Ability Predicts Cortical Structure and Covariance in Boys with Autism Spectrum Disorder. Cereb cortex 27:1849\u0026ndash;1862. https://doi.org/10.1093/CERCOR/BHW024\u003c/li\u003e\n\u003cli\u003eArutiunian V, Davydova E, Pereverzeva D, et al (2023) Reduced grey matter volume of amygdala and hippocampus is associated with the severity of autistic symptoms and language abilities in school-aged children with Autism Spectrum Disorder: an exploratory study. Brain Struct Funct 228:1573\u0026ndash;1579. https://doi.org/10.1007/s00429-023-02660-9\u003c/li\u003e\n\u003cli\u003eNaigles LR, Johnson R, Mastergeorge A, et al (2017) Neural Correlates of Language Variability in Preschool-Aged Boys with Autism Spectrum Disorder. Autism Res 10:1107. https://doi.org/10.1002/AUR.1756\u003c/li\u003e\n\u003cli\u003eArutiunian V, Gomozova M, Minnigulova A, et al (2023) Structural brain abnormalities and their association with language impairment in school-aged children with Autism Spectrum Disorder. Sci Rep 13:1\u0026ndash;15. https://doi.org/10.1038/s41598-023-28463-w\u003c/li\u003e\n\u003cli\u003eYang Y (2016) Rating Scales For Children\u0026rsquo;s Developmental Behavior and Mental Health, 1st ed. People\u0026rsquo;s Medical Publishing House, Beijing\u003c/li\u003e\n\u003cli\u003eKrug DA, Arick J, Almond P (1980) Behavior checklist for identifying severely handicapped individuals with high levels of autistic behavior. J Child Psychol Psychiatry 21:221\u0026ndash;229. https://doi.org/10.1111/J.1469-7610.1980.TB01797.X\u003c/li\u003e\n\u003cli\u003eYoon U, Fonov VS, Perusse D, Evans AC (2009) The effect of template choice on morphometric analysis of pediatric brain data. Neuroimage 45:769\u0026ndash;777. https://doi.org/10.1016/J.NEUROIMAGE.2008.12.046\u003c/li\u003e\n\u003cli\u003eWilke M, Altaye M, Holland SK (2017) CerebroMatic: A versatile toolbox for spline-based MRI template creation. Front Comput Neurosci 11:5. https://doi.org/10.3389/fncom.2017.00005\u003c/li\u003e\n\u003cli\u003eWilke M (2018) A spline-based regression parameter set for creating customized DARTEL MRI brain templates from infancy to old age. Data Br 16:959\u0026ndash;966. https://doi.org/10.1016/J.DIB.2017.12.001\u003c/li\u003e\n\u003cli\u003eYan CG, Wang X Di, Zuo XN, Zang YF (2016) DPABI: Data Processing \u0026amp; Analysis for (Resting-State) Brain Imaging. Neuroinformatics 14:339\u0026ndash;351. https://doi.org/10.1007/S12021-016-9299-4\u003c/li\u003e\n\u003cli\u003eFriederici AD (2011) The brain basis of language processing: from structure to function. Physiol Rev 91:1357\u0026ndash;1392. https://doi.org/10.1152/PHYSREV.00006.2011\u003c/li\u003e\n\u003cli\u003eHickok G, Poeppel D (2007) The cortical organization of speech processing. Nat Rev Neurosci 8:393\u0026ndash;402. https://doi.org/10.1038/nrn2113\u003c/li\u003e\n\u003cli\u003eRedcay E (2008) The superior temporal sulcus performs a common function for social and speech perception: implications for the emergence of autism. Neurosci Biobehav Rev 32:123\u0026ndash;142. https://doi.org/10.1016/J.NEUBIOREV.2007.06.004\u003c/li\u003e\n\u003cli\u003eLiu J, Yao L, Zhang W, et al (2017) Gray matter abnormalities in pediatric autism spectrum disorder: a meta-analysis with signed differential mapping. Eur Child Adolesc Psychiatry 26:933\u0026ndash;945. https://doi.org/10.1007/S00787-017-0964-4\u003c/li\u003e\n\u003cli\u003eToal F, Daly EM, Page L, et al (2010) Clinical and anatomical heterogeneity in autistic spectrum disorder: A structural MRI study. Psychol Med 40:1171\u0026ndash;1181. https://doi.org/10.1017/S0033291709991541\u003c/li\u003e\n\u003cli\u003eXiao Y, Friederici AD, Margulies DS, Brauer J (2016) Longitudinal changes in resting-state fMRI from age 5 to age 6 years covary with language development. Neuroimage 128:116\u0026ndash;124. https://doi.org/10.1016/J.NEUROIMAGE.2015.12.008\u003c/li\u003e\n\u003cli\u003eDehaene-Lambertz G, Dehaene S, Hertz-Pannier L (2002) Functional neuroimaging of speech perception in infants. Science 298:2013\u0026ndash;2015. https://doi.org/10.1126/science.1077066\u003c/li\u003e\n\u003cli\u003eRedcay E, Haist F, Courchesne E (2008) Functional neuroimaging of speech perception during a pivotal period in language acquisition. Dev Sci 11:237\u0026ndash;252. https://doi.org/10.1111/j.1467-7687.2008.00674.x\u003c/li\u003e\n\u003cli\u003eDuff MC, Brown-Schmidt S (2012) The hippocampus and the flexible use and processing of language. Front Hum Neurosci 6:1\u0026ndash;11. https://doi.org/10.3389/fnhum.2012.00069\u003c/li\u003e\n\u003cli\u003ePiai V, Anderson KL, Lin JJ, et al (2016) Direct brain recordings reveal hippocampal rhythm underpinnings of language processing. Proc Natl Acad Sci U S A 113:11366\u0026ndash;11371. https://doi.org/10.1073/pnas.1603312113\u003c/li\u003e\n\u003cli\u003eMacKay DG, Stewart R, Burke DM (1998) H.M. revisited: relations between language comprehension, memory, and the hippocampal system. J Cogn Neurosci 10:377\u0026ndash;394. https://doi.org/10.1162/089892998562807\u003c/li\u003e\n\u003cli\u003eMacKay DG, Burke DM, Stewart R (1998) H.M.\u0026rsquo;s Language Production Deficits: Implications for Relations between Memory, Semantic Binding, and the Hippocampal System. J Mem Lang 38:28\u0026ndash;69. https://doi.org/10.1006/JMLA.1997.2544\u003c/li\u003e\n\u003cli\u003eKlooster NB, Duff MC (2015) Remote semantic memory is impoverished in hippocampal amnesia. Neuropsychologia 79:42\u0026ndash;52. https://doi.org/10.1016/J.NEUROPSYCHOLOGIA.2015.10.017\u003c/li\u003e\n\u003cli\u003eBanker SM, Gu X, Schiller D, Foss-Feig JH (2021) Hippocampal contributions to social and cognitive deficits in autism spectrum disorder. Trends Neurosci 44:793. https://doi.org/10.1016/J.TINS.2021.08.005\u003c/li\u003e\n\u003cli\u003eLong J, Li H, Liu Y, et al (2024) Insights into the structure and function of the hippocampus: implications for the pathophysiology and treatment of autism spectrum disorder. Front Psychiatry 15:1364858. https://doi.org/10.3389/FPSYT.2024.1364858\u003c/li\u003e\n\u003cli\u003eLee JK, Nordahl CW, Amaral DG, et al (2015) Assessing hippocampal development and language in early childhood: Evidence from a new application of the Automatic Segmentation Adapter Tool. Hum Brain Mapp 36:4483\u0026ndash;4496. https://doi.org/10.1002/HBM.22931\u003c/li\u003e\n\u003cli\u003ePrice C (2010) The anatomy of language: a review of 100 fMRI studies published in 2009. Ann N Y Acad Sci 1191:62\u0026ndash;88. https://doi.org/10.1111/J.1749-6632.2010.05444.X\u003c/li\u003e\n\u003cli\u003eBinder JR, Desai RH, Graves WW, Conant LL (2009) Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cereb cortex 19:2767\u0026ndash;2796. https://doi.org/10.1093/CERCOR/BHP055\u003c/li\u003e\n\u003cli\u003eZoccante L, Viviani A, Ferro A, et al (2010) Increased left parietal volumes relate to delayed language development in autism: a structural MRI study. Funct Neurol 25:217\u0026ndash;221\u003c/li\u003e\n\u003cli\u003eHyde KL, Samson F, Evans AC, Mottron L (2010) Neuroanatomical differences in brain areas implicated in perceptual and other core features of autism revealed by cortical thickness analysis and voxel-based morphometry. Hum Brain Mapp 31:556\u0026ndash;566. https://doi.org/10.1002/HBM.20887\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-child-and-adolescent-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ecap","sideBox":"Learn more about [European Child \u0026 Adolescent Psychiatry](http://link.springer.com/journal/787)","snPcode":"787","submissionUrl":"https://submission.nature.com/new-submission/787/3","title":"European Child \u0026 Adolescent Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Autism spectrum disorder, young children, language ability, grey matter volume, superior temporal gyrus, hippocampus","lastPublishedDoi":"10.21203/rs.3.rs-4673621/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4673621/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChildren with autism spectrum disorder (ASD) are highly heterogenous in their language abilities. A number of studies have shown neural correlates of language deficits in children with ASD, but the underlying neuroanatomical foundation of early language deficits in ASD remains largely elusive. In this study, we analyzed MRI data from a cohort of Chinese children with ASD (n\u0026thinsp;=\u0026thinsp;67) and typical development (TD, n\u0026thinsp;=\u0026thinsp;37) aged 1.5 to 6.5 years. The ASD sample was classified into two subgroups based on the median of the language scores: ASD with moderate language deficits (ASD\u003csub\u003emoderate\u003c/sub\u003e, n\u0026thinsp;=\u0026thinsp;34) and ASD with severe language deficits (ASD\u003csub\u003esevere\u003c/sub\u003e, n\u0026thinsp;=\u0026thinsp;34). We tested the group differences in the brain volumes between TD and two ASD subgroups, and also examined the associations between cortical grey matter volume and language abilities in TD and ASD subgroups, separately. We observed significant group differences in grey matter and white matter volume, with post-hoc analyses specifically indicating significant differences between TD and ASD\u003csub\u003emoderate\u003c/sub\u003e subgroup. Significant correlations between grey matter volume and language scores were observed exclusively within the ASD\u003csub\u003emoderate\u003c/sub\u003e subgroup, including positive associations in the bilateral superior temporal gyrus, hippocampus, and left inferior parietal lobe, and negative correlations in the bilateral precuneus. These findings provide novel evidence for the neuroanatomical basis related to language ability in an ASD subgroup with moderate language deficits, and offer new insights into the heterogeneity of language deficits in children with ASD.\u003c/p\u003e","manuscriptTitle":"Neuroanatomical Basis of Language Ability in an Autism Subgroup with Moderate Language Deficits","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-26 16:59:23","doi":"10.21203/rs.3.rs-4673621/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-06T05:36:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-06T03:47:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-07T02:42:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194688067273943004342066842066608296374","date":"2024-08-06T08:58:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207365425987358362776883579810686865929","date":"2024-07-31T03:40:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-23T17:13:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-03T05:44:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-03T05:43:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Child \u0026 Adolescent Psychiatry","date":"2024-07-02T10:46:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-child-and-adolescent-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ecap","sideBox":"Learn more about [European Child \u0026 Adolescent Psychiatry](http://link.springer.com/journal/787)","snPcode":"787","submissionUrl":"https://submission.nature.com/new-submission/787/3","title":"European Child \u0026 Adolescent Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7c1dbd2a-9c20-4f93-995f-e1720efe6e2c","owner":[],"postedDate":"July 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-11T15:59:26+00:00","versionOfRecord":{"articleIdentity":"rs-4673621","link":"https://doi.org/10.1007/s00787-024-02605-5","journal":{"identity":"european-child-and-adolescent-psychiatry","isVorOnly":false,"title":"European Child \u0026 Adolescent Psychiatry"},"publishedOn":"2024-11-08 15:57:05","publishedOnDateReadable":"November 8th, 2024"},"versionCreatedAt":"2024-07-26 16:59:23","video":"","vorDoi":"10.1007/s00787-024-02605-5","vorDoiUrl":"https://doi.org/10.1007/s00787-024-02605-5","workflowStages":[]},"version":"v1","identity":"rs-4673621","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4673621","identity":"rs-4673621","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}