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Del Bianco T, Lai MC, Mason L, Johnson MH, Charman T, Loth E, Banaschewski T, Buitelaar J, Murphy DGM, Jones EJH. Sex differences in social brain neural responses in autism: temporal profiles of configural face-processing within data-driven time windows. Sci Rep 2024; 14:14038. [PMID: 38890406 PMCID: PMC11189412 DOI: 10.1038/s41598-024-64387-9] [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: 12/11/2023] [Accepted: 06/07/2024] [Indexed: 06/20/2024] Open
Abstract
Face-processing timing differences may underlie visual social attention differences between autistic and non-autistic people, and males and females. This study investigates the timing of the effects of neurotype and sex on face-processing, and their dependence on age. We analysed EEG data during upright and inverted photographs of faces from 492 participants from the Longitudinal European Autism Project (141 neurotypical males, 76 neurotypical females, 202 autistic males, 73 autistic females; age 6-30 years). We detected timings of sex/diagnosis effects on event-related potential amplitudes at the posterior-temporal channel P8 with Bootstrapped Cluster-based Permutation Analysis and conducted Growth Curve Analysis (GCA) to investigate the timecourse and dependence on age of neural signals. The periods of influence of neurotype and sex overlapped but differed in onset (respectively, 260 and 310 ms post-stimulus), with sex effects lasting longer. GCA revealed a smaller and later amplitude peak in autistic female children compared to non-autistic female children; this difference decreased in adolescence and was not significant in adulthood. No age-dependent neurotype difference was significant in males. These findings indicate that sex and neurotype influence longer latency face processing and implicates cognitive rather than perceptual processing. Sex may have more overarching effects than neurotype on configural face processing.
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Affiliation(s)
- Teresa Del Bianco
- Centre for Brain and Cognitive Development, Henry Wellcome Building, Birkbeck University of London, Malet Street, London, WC1E 7HX, UK.
- School of Social Sciences and Professions, London Metropolitan University, London, UK.
| | - Meng-Chuan Lai
- Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Luke Mason
- Centre for Brain and Cognitive Development, Henry Wellcome Building, Birkbeck University of London, Malet Street, London, WC1E 7HX, UK
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Mark H Johnson
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Tony Charman
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Eva Loth
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | | | - Jan Buitelaar
- Donders Center of Medical Neurosciences, Radboud University, Nijmegen, The Netherlands
| | - Declan G M Murphy
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Emily J H Jones
- Centre for Brain and Cognitive Development, Henry Wellcome Building, Birkbeck University of London, Malet Street, London, WC1E 7HX, UK
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Ke SY, Wu H, Sun H, Zhou A, Liu J, Zheng X, Liu K, Westover MB, Xu H, Kong XJ. Classification of autism spectrum disorder using electroencephalography in Chinese children: a cross-sectional retrospective study. Front Neurosci 2024; 18:1330556. [PMID: 38332856 PMCID: PMC10850305 DOI: 10.3389/fnins.2024.1330556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 01/09/2024] [Indexed: 02/10/2024] Open
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by diverse clinical features. EEG biomarkers such as spectral power and functional connectivity have emerged as potential tools for enhancing early diagnosis and understanding of the neural processes underlying ASD. However, existing studies yield conflicting results, necessitating a comprehensive, data-driven analysis. We conducted a retrospective cross-sectional study involving 246 children with ASD and 42 control children. EEG was collected, and diverse EEG features, including spectral power and spectral coherence were extracted. Statistical inference methods, coupled with machine learning models, were employed to identify differences in EEG features between ASD and control groups and develop classification models for diagnostic purposes. Our analysis revealed statistically significant differences in spectral coherence, particularly in gamma and beta frequency bands, indicating elevated long range functional connectivity between frontal and parietal regions in the ASD group. Machine learning models achieved modest classification performance of ROC-AUC at 0.65. While machine learning approaches offer some discriminative power classifying individuals with ASD from controls, they also indicate the need for further refinement.
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Affiliation(s)
- Si Yang Ke
- Anthinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, United States
| | - Huiwen Wu
- Hubei Maternity and Child Health Hospital, Wuhan, Hubei, China
| | - Haoqi Sun
- Anthinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Aiqin Zhou
- Hubei Maternity and Child Health Hospital, Wuhan, Hubei, China
| | - Jianhua Liu
- Huangshi Maternity and Child Health Care Hospital, Huangshi, Hubei, China
| | - Xiaoyun Zheng
- Hubei Maternity and Child Health Hospital, Wuhan, Hubei, China
| | - Kevin Liu
- Anthinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - M. Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, United States
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Haiqing Xu
- Hubei Maternity and Child Health Hospital, Wuhan, Hubei, China
| | - Xue-jun Kong
- Anthinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Beth Israel Deaconess Medical Center, Boston, MA, United States
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