Sex differences in structural brain asymmetry of children with autism spectrum disorders

Li, Cuicui; Ning, Mingmin; Fang, Pinyan; Xu, Huibi

doi:10.31083/j.jin2002032

Cited by 8 publications

(6 citation statements)

References 38 publications

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“…The reported multiregional asymmetry alterations are consistent with the notion that laterality is easily disrupted in patients with ASD (Maximo et al, 2014). In our study, the results showed that various GM asymmetry alterations were located in several clusters in the general ASD group, predominantly involving the temporal, frontal, and parahippocampal gyrus, which part overlapped with findings in previous studies, such as the parahippocampal gyrus having more leftward asymmetry (Li et al, 2021). However, in the postcentral gyrus, general ASD showed more rightward asymmetry; previous studies by Hau et al (2022) reported leftward asymmetry of the postcentral gyrus volume in ASD patients relative to the TD group.…”

Section: The Atypical Pattern Of Gm Asymmetry In the General Asd Groupsupporting

confidence: 91%

“…Abnormal structural asymmetries in patients with ASD have been reported to be widely distributed across various brain regions ( Maximo et al, 2014 ). Examples include significantly increased rightward asymmetry in the inferior parietal lobule, auditory cortex ( Floris et al, 2016 ), posterior superior temporal gyrus ( Gage et al, 2009 ), and lateral orbitofrontal surface area ( Postema et al, 2019 ) and reduced rightward asymmetry in the medial orbitofrontal surface area, putamen volume, and the parahippocampal gyrus ( Postema et al, 2019 ; Li et al, 2021 ). The reported multiregional asymmetry alterations are consistent with the notion that laterality is easily disrupted in patients with ASD ( Maximo et al, 2014 ).…”

Section: Discussionmentioning

confidence: 99%

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Gray matter asymmetry atypical patterns in subgrouping minors with autism based on core symptoms

Chen

et al. 2023

Front. Neurosci.

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Abnormal gray matter (GM) asymmetry has been verified in autism spectrum disorder (ASD), which is characterized by high heterogeneity. ASD is distinguished by three core symptom domains. Previous neuroimaging studies have offered support for divergent neural substrates of different core symptom domains in ASD. However, no previous study has explored GM asymmetry alterations underlying different core symptom domains. This study sought to clarify atypical GM asymmetry patterns underlying three core symptom domains in ASD with a large sample of 230 minors with ASD (ages 7–18 years) and 274 matched TD controls from the Autism Brain Imaging Data Exchange I (ABIDE I) repository. To this end, the scores of the revised autism diagnostic interview (ADI-R) subscales were normalized for grouping ASD into three core-symptom-defined subgroups: social interaction (SI), verbal communication (VA), and restricted repetitive behaviors (RRB). We investigated core-symptom-related GM asymmetry alterations in ASD resulting from advanced voxel-based morphometry (VBM) by general linear models. We also examined the relationship between GM asymmetry and age and between GM asymmetry and symptom severity assessed by the Autism Diagnostic Observation Schedule (ADOS). We found unique GM asymmetry alterations underlying three core-symptom-defined subgroups in ASD: more rightward asymmetry in the thalamus for SI, less rightward asymmetry in the superior temporal gyrus, anterior cingulate and caudate for VA, and less rightward asymmetry in the middle and inferior frontal gyrus for RRB. Furthermore, the asymmetry indexes in the thalamus were negatively associated with ADOS_SOCIAL scores in the general ASD group. We also showed significant correlations between GM asymmetry and age in ASD and TD individuals. Our results support the theory that each core symptom domain of ASD may have independent etiological and neurobiological underpinnings, which is essential for the interpretation of heterogeneity and the future diagnosis and treatment of ASD.

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Section: The Atypical Pattern Of Gm Asymmetry In the General Asd Groupsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Gray matter asymmetry atypical patterns in subgrouping minors with autism based on core symptoms

Chen

et al. 2023

Front. Neurosci.

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“…While environmental factors like family environment or intense training (e.g., music practice [ 55 ]) are known to modulate brain anatomy [ 56 ], little is known about schooling experience. More specifically, recent work has shown that while the cortical thickness (CTh) of the left and right hemispheres is globally similar (i.e., symmetric), between-hemisphere variations (i.e., asymmetry index) can be observed, and they provide insights about typical or atypical neurodevelopment [ 17 , 19 , 57 , 58 ]. The current study specifically contrasted CTh asymmetries in 4–18 y.o.…”

Section: Discussionmentioning

confidence: 99%

“…Past work on brain asymmetry was mostly performed using adult participants’ data, reporting rather symmetric hemispheres [ 16 , 17 ]. While other studies have investigated asymmetry in atypical children [ 19 , 57 , 58 ], few report developmental data on CTh asymmetry metrics. Hill et al (2010) compared healthy babies with healthy adults and found no difference in terms of cortical folding patterns and hemispherical depth asymmetries.…”

Section: Discussionmentioning

confidence: 99%

Learning by Heart or with Heart: Brain Asymmetry Reflects Pedagogical Practices

Schetter,

Romascano,

Gaujard

et al. 2023

Brain Sciences

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Brain hemispheres develop rather symmetrically, except in the case of pathology or intense training. As school experience is a form of training, the current study tested the influence of pedagogy on morphological development through the cortical thickness (CTh) asymmetry index (AI). First, we compared the CTh AI of 111 students aged 4 to 18 with 77 adults aged > 20. Second, we investigated the CTh AI of the students as a function of schooling background (Montessori or traditional). At the whole-brain level, CTh AI was not different between the adult and student groups, even when controlling for age. However, pedagogical experience was found to impact CTh AI in the temporal lobe, within the parahippocampal (PHC) region. The PHC region has a functional lateralization, with the right PHC region having a stronger involvement in spatiotemporal context encoding, while the left PHC region is involved in semantic encoding. We observed CTh asymmetry toward the left PHC region for participants enrolled in Montessori schools and toward the right for participants enrolled in traditional schools. As these participants were matched on age, intelligence, home-life and socioeconomic conditions, we interpret this effect found in memory-related brain regions to reflect differences in learning strategies. Pedagogy modulates how new concepts are encoded, with possible long-term effects on knowledge transfer.

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“…In the literature [14] multi-site data with 900 subjects was used, and about 200 subjects from 2 sites were included in the paper [15]. Papers [16,17] show that, compared with a small number of samples in a single area, MRI data of the same kind of brain diseases in multiple sites can provide more sufficient statistical information, so as to better explore the functional mode of the brain structure of a patient. By studying the papers [18][19][20], it can be found that compared with the patients in a single area, the population distribution of the same disease in different regions is diverse.…”

Section: Introductionmentioning

confidence: 99%

Using Regularized Multi-Task Learning for Schizophrenia MRI Data Classification

Wang¹,

Shi²,

Xiao³

2022

J. Integr. Neurosci.

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Background: Machine learning techniques and magnetic resonance imaging methods have been widely used in computer-aided diagnosis and prognosis of severe brain diseases such as schizophrenia, Alzheimer, etc. Methods: In this paper, a regularized multi-task learning method for schizophrenia classification is proposed, and three MRI datasets of schizophrenia, collected from different data centers, are investigated. Firstly, slice extraction is used in image preprocessing. Then texture features of gray-level co-occurrence matrices are extracted from the above processed images. Finally, a p-norm regularized multi-task learning method is proposed to simultaneously learn the site-specific and site-shared features of the multi-site data, which can effectively discriminate schizophrenia patients from normal controls. Results: The classification error rate on 10 datasets can be reduced from 10% to 30%. Conclusions: The proposed method obtains excellent results and provides objective evidence for clinical diagnosis and treatment of schizophrenia.

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Sex differences in structural brain asymmetry of children with autism spectrum disorders

Abstract: specific diagnostic methods and treatments for males and females children with autism spectrum disorders.

Cited by 8 publications

References 38 publications

Gray matter asymmetry atypical patterns in subgrouping minors with autism based on core symptoms

Gray matter asymmetry atypical patterns in subgrouping minors with autism based on core symptoms

Learning by Heart or with Heart: Brain Asymmetry Reflects Pedagogical Practices

Using Regularized Multi-Task Learning for Schizophrenia MRI Data Classification

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