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Ortug A, Guo Y, Feldman HA, Ou Y, Warren JLA, Dieuveuil H, Baumer NT, Faja SK, Takahashi E. Autism-associated brain differences can be observed in utero using MRI. Cereb Cortex 2024; 34:bhae117. [PMID: 38602735 PMCID: PMC11008691 DOI: 10.1093/cercor/bhae117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 03/01/2024] [Accepted: 03/02/2024] [Indexed: 04/12/2024] Open
Abstract
Developmental changes that occur before birth are thought to be associated with the development of autism spectrum disorders. Identifying anatomical predictors of early brain development may contribute to our understanding of the neurobiology of autism spectrum disorders and allow for earlier and more effective identification and treatment of autism spectrum disorders. In this study, we used retrospective clinical brain magnetic resonance imaging data from fetuses who were diagnosed with autism spectrum disorders later in life (prospective autism spectrum disorders) in order to identify the earliest magnetic resonance imaging-based regional volumetric biomarkers. Our results showed that magnetic resonance imaging-based autism spectrum disorder biomarkers can be found as early as in the fetal period and suggested that the increased volume of the insular cortex may be the most promising magnetic resonance imaging-based fetal biomarker for the future emergence of autism spectrum disorders, along with some additional, potentially useful changes in regional volumes and hemispheric asymmetries.
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Affiliation(s)
- Alpen Ortug
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Yurui Guo
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Henry A Feldman
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Yangming Ou
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Jose Luis Alatorre Warren
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Harrison Dieuveuil
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Nicole T Baumer
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Susan K Faja
- Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Division of Developmental Medicine, Laboratories of Cognitive Neuroscience, Boston Children's Hospital, Harvard Medical School, Brookline, MA 02115, United States
| | - Emi Takahashi
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
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Xu K, Sun Z, Qiao Z, Chen A. Diagnosing autism severity associated with physical fitness and gray matter volume in children with autism spectrum disorder: Explainable machine learning method. Complement Ther Clin Pract 2024; 54:101825. [PMID: 38169278 DOI: 10.1016/j.ctcp.2023.101825] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/18/2023] [Accepted: 12/29/2023] [Indexed: 01/05/2024]
Abstract
PURPOSE This study aimed to investigate the relationship between physical fitness, gray matter volume (GMV), and autism severity in children with autism spectrum disorder (ASD). Besides, we sought to diagnose autism severity associated with physical fitness and GMV using machine learning methods. METHODS Ninety children diagnosed with ASD underwent physical fitness tests, magnetic resonance imaging scans, and autism severity assessments. Diagnosis models were established using extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), and decision tree (DT) algorithms. Hyperparameters were optimized through the grid search cross-validation method. The shapley additive explanation (SHAP) method was employed to explain the diagnosis results. RESULTS Our study revealed associations between muscular strength in physical fitness and GMV in specific brain regions (left paracentral lobule, bilateral thalamus, left inferior temporal gyrus, and cerebellar vermis I-II) with autism severity in children with ASD. The accuracy (95 % confidence interval) of the XGB, RF, SVM, and DT models were 77.9 % (77.3, 78.6 %), 72.4 % (71.7, 73.2 %), 71.9 % (71.1, 72.6 %), and 66.9 % (66.2, 67.7 %), respectively. SHAP analysis revealed that muscular strength and thalamic GMV significantly influenced the decision-making process of the XGB model. CONCLUSION Machine learning methods can effectively diagnose autism severity associated with physical fitness and GMV in children with ASD. In this respect, the XGB model demonstrated excellent performance across various indicators, suggesting its potential for diagnosing autism severity.
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Affiliation(s)
- Keyun Xu
- College of Physical Education, Yangzhou University, Yangzhou, 225127, China
| | - Zhiyuan Sun
- College of Physical Education, Yangzhou University, Yangzhou, 225127, China
| | - Zhiyuan Qiao
- College of Physical Education, Yangzhou University, Yangzhou, 225127, China
| | - Aiguo Chen
- Nanjing Sport Institute, Nanjing, 210014, China.
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Faraji R, Ganji Z, Khandan Khadem Z, Akbari-Lalimi H, Eidy F, Zare H. Volume-based and Surface-Based Methods in Autism Compared with Healthy Controls Are Free surfer and CAT12 in Agreement? IRANIAN JOURNAL OF CHILD NEUROLOGY 2024; 18:93-118. [PMID: 38375127 PMCID: PMC10874516 DOI: 10.22037/ijcn.v18i1.43294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/07/2023] [Indexed: 02/21/2024]
Abstract
Objectives Autism Spectrum Disorder (ASD) encompasses a range of neurodevelopmental disorders, and early detection is crucial. This study aims to identify the Regions of Interest (ROIs) with significant differences between healthy controls and individuals with autism, as well as evaluate the agreement between FreeSurfer 6 (FS6) and Computational Anatomy Toolbox (CAT12) methods. Materials & Methods Surface-based and volume-based features were extracted from FS software and CAT12 toolbox for Statistical Parametric Mapping (SPM) software to estimate ROI-wise biomarkers. These biomarkers were compared between 18 males Typically Developing Controls (TDCs) and 40 male subjects with ASD to assess group differences for each method. Finally, agreement and regression analyses were performed between the two methods for TDCs and ASD groups. Results Both methods revealed ROIs with significant differences for each parameter. The Analysis of Covariance (ANCOVA) showed that both TDCs and ASD groups indicated a significant relationship between the two methods (p<0.001). The R2 values for TDCs and ASD groups were 0.692 and 0.680, respectively, demonstrating a moderate correlation between CAT12 and FS6. Bland-Altman graphs showed a moderate level of agreement between the two methods. Conclusion The moderate correlation and agreement between CAT12 and FS6 suggest that while some consistency is observed in the results, CAT12 is not a superior substitute for FS6 software. Further research is needed to identify a potential replacement for this method.
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Affiliation(s)
- Reyhane Faraji
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zohreh Ganji
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zahra Khandan Khadem
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hossein Akbari-Lalimi
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Fereshteh Eidy
- Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Hoda Zare
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
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Shen L, Zhang J, Fan S, Ping L, Yu H, Xu F, Cheng Y, Xu X, Yang C, Zhou C. Cortical thickness abnormalities in autism spectrum disorder. Eur Child Adolesc Psychiatry 2024; 33:65-77. [PMID: 36542200 DOI: 10.1007/s00787-022-02133-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022]
Abstract
The pathological mechanism of autism spectrum disorder (ASD) remains unclear. Nowadays, surface-based morphometry (SBM) based on structural magnetic resonance imaging (sMRI) techniques have reported cortical thickness (CT) variations in ASD. However, the findings were inconsistent and heterogeneous. This current meta-analysis conducted a whole-brain vertex-wise coordinate-based meta-analysis (CBMA) on CT studies to explore the most noticeable and robust CT changes in ASD individuals by applying the seed-based d mapping (SDM) program. A total of 26 investigations comprised 27 datasets were included, containing 1,635 subjects with ASD and 1470 HC, along with 94 coordinates. Individuals with ASD exhibited significantly altered CT in several regions compared to HC, including four clusters with thicker CT in the right superior temporal gyrus (STG.R), the left middle temporal gyrus (MTG.L), the left anterior cingulate/paracingulate gyri, the right superior frontal gyrus (SFG.R, medial orbital parts), as well as three clusters with cortical thinning including the left parahippocampal gyrus (PHG.L), the right precentral gyrus (PCG.R) and the left middle frontal gyrus (MFG.L). Adults with ASD only demonstrated CT thinning in the right parahippocampal gyrus (PHG.R), revealed by subgroup meta-analyses. Meta-regression analyses found that CT in STG.R was positively correlated with age. Meanwhile, CT in MFG.L and PHG.L had negative correlations with the age of ASD individuals. These results suggested a complicated and atypical cortical development trajectory in ASD, and would provide a deeper understanding of the neural mechanism underlying the cortical morphology in ASD.
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Affiliation(s)
- Liancheng Shen
- Department of Psychiatry, Shandong Daizhuang Hospital, Jining, China
| | - Junqing Zhang
- Department of Pharmacy, Shandong Daizhuang Hospital, Jining, China
| | - Shiran Fan
- School of Mental Health, Jining Medical University, Jining, China
| | - Liangliang Ping
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Hao Yu
- School of Mental Health, Jining Medical University, Jining, China
| | - Fangfang Xu
- School of Mental Health, Jining Medical University, Jining, China
| | - Yuqi Cheng
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiufeng Xu
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Chunyan Yang
- School of Rehabilitation Medicine, Jining Medical University, Jining, China.
| | - Cong Zhou
- School of Mental Health, Jining Medical University, Jining, China.
- Department of Psychology, Affiliated Hospital of Jining Medical University, Jining, China.
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Kara MZ, Örüm MH, Karadağ AS, Kalenderoğlu A, Kara A. Reduction in Retinal Ganglion Cell Layer, Inner Plexiform Layer, and Choroidal Thickness in Children With Autism Spectrum Disorder. Cureus 2023; 15:e49981. [PMID: 38179343 PMCID: PMC10766208 DOI: 10.7759/cureus.49981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/28/2023] [Indexed: 01/06/2024] Open
Abstract
PURPOSES The aim of this study was to evaluate the retinal nerve fiber layer (RNFL), choroidal layer, inner plexiform layer (IPL), and ganglion cell layer (GCL) in patients with autism spectrum disorder (ASD). METHODS In this study, we measured the thickness of the RNFL, GCL, IPL, and choroidal thickness using a spectral optical coherence tomography (OCT) device and we compared the results between the children diagnosed with ASD and healthy controls. Correlation between the Childhood Autism Rating Scale (CARS) and the OCT data was evaluated. RESULTS Both ASD and control group consisted of 40 subjects (30 males and 10 females). Of the children in the ASD group, 29 had normal intelligence and 11 had mild intellectual disability (MID). The mean age of patients in the ASD group and control groups were 9.77 ± 3.37 years and 9.85 ± 3.97 years (p = 0.928). There was a statistically significant difference between the ASD group and the control group in the nasal and nasal-superior sectors of the RNFL layers in the left eye when all the lower layers of RNFL were assessed. In both eyes, the children with ASD had considerably lower mean choroidal thicknesses than the controls. When compared to the controls, the GCL and IPL volumes in the individuals with ASD were considerably lower in both eyes. Compared to the MID group, the left GCL volume of the nasal-inferior group was noticeably higher. A significant correlation was found between CARS scores and left GCL left IPL. CONCLUSIONS In contrast to RNFL in the ASD group, significant reductions in IPL, GCL, and choroidal thickness were observed in both eyes. It is thought that GCL may be a much more important biomarker than RNFL in terms of representing the structural deterioration in the brain. In addition, these results may form the basis for a new perspective on the use of OCT for the diagnosis and clinical course of autism.
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Affiliation(s)
- Mahmut Zabit Kara
- Child Adolescent Psychiatry, University of Health Sciences, Antalya, TUR
| | | | | | | | - Aslıhan Kara
- Biological Sciences, Semikal Technology, Antalya, TUR
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Cheng W, Sun Z, Cai K, Wu J, Dong X, Liu Z, Shi Y, Yang S, Zhang W, Chen A. Relationship between Overweight/Obesity and Social Communication in Autism Spectrum Disorder Children: Mediating Effect of Gray Matter Volume. Brain Sci 2023; 13:brainsci13020180. [PMID: 36831723 PMCID: PMC9954689 DOI: 10.3390/brainsci13020180] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/10/2023] [Accepted: 01/17/2023] [Indexed: 01/24/2023] Open
Abstract
With advances in medical diagnostic technology, the healthy development of children with autism spectrum disorder (ASD) is receiving more and more attention. In this article, the mediating effect of brain gray matter volume (GMV) between overweight/obesity and social communication (SC) was investigated through the analysis of the relationship between overweight/obesity and SC in autism spectrum disorder children. In total, 101 children with ASD aged 3-12 years were recruited from three special educational centers (Yangzhou, China). Overweight/obesity in children with ASD was indicated by their body mass index (BMI); the Social Responsiveness Scale, Second Edition (SRS-2) was used to assess their social interaction ability, and structural Magnetic Resonance Imaging (sMRI) was used to measure GMV. A mediation model was constructed using the Process plug-in to analyze the mediating effect of GMV between overweight/obesity and SC in children with ASD. The results revealed that: overweight/obesity positively correlated with SRS-2 total points (p = 0.01); gray matter volume in the left dorsolateral superior frontal gyrus (Frontal_Sup_L GMV) negatively correlated with SRS-2 total points (p = 0.001); and overweight/obesity negatively correlated with Frontal_Sup_L GMV (p = 0.001). The Frontal_Sup_L GMV played a partial mediating role in the relationship between overweight/obesity and SC, accounting for 36.6% of total effect values. These findings indicate the significant positive correlation between overweight/obesity and SC; GMV in the left dorsolateral superior frontal gyrus plays a mediating role in the relationship between overweight/obesity and SC. The study may provide new evidence toward comprehensively revealing the overweight/obesity and SC relationship.
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Affiliation(s)
- Wei Cheng
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Zhiyuan Sun
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Kelong Cai
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Jingjing Wu
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Xiaoxiao Dong
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Zhimei Liu
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Yifan Shi
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Sixin Yang
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Weike Zhang
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
| | - Aiguo Chen
- College of Physical Education, Yangzhou University, Yangzhou 225127, China
- Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China
- Correspondence: ; Tel.: +86-139-5272-5968
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