American Psychiatric Association, D. S. M. T. F., & American Psychiatric Association, D. S. Diagnostic and statistical manual of mental disorders: DSM-5 (Vol. 5, No. 5). Washington, DC: American psychiatric association (2013).

Lai MC, Lombardo MV, Ruigrok AN, Chakrabarti B, Auyeung B, Szatmari P, MRC AIMS Consortium. Quantifying and exploring camouflaging in men and women with autism. Autism. 2016;21:690–702.

PubMed  PubMed Central  Google Scholar 

Maenner MJ. Prevalence of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2016. MMWR Surveill Summ. 2020;69:1–12.

PubMed  PubMed Central  Google Scholar 

Dawson G. Early behavioral intervention, brain plasticity, and the prevention of autism spectrum disorder. Dev Psychopathol. 2008;20(3):775–803.

PubMed  Google Scholar 

Lord C, Elsabbagh M, Baird G, Veenstra-Vanderweele J. Autism spectrum disorder. Lancet. 2018;392(10146):508–20.

PubMed  PubMed Central  Google Scholar 

Hernandez LM, Rudie JD, Green SA, Bookheimer S, Dapretto M. Neural signatures of autism spectrum disorders: insights into brain network dynamics. Neuropsychopharmacology. 2015;40(1):171–89.

PubMed  Google Scholar 

Moridian P, Ghassemi N, Jafari M, Salloum-Asfar S, Sadeghi D, Khodatars M, Acharya UR. Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: a review. Front Mol Neurosci. 2022;15:999605.

PubMed  PubMed Central  Google Scholar 

Zaharchuk G, Gong E, Wintermark M, Rubin D, Langlotz CP. Deep learning in neuroradiology. Am J Neuroradiol. 2018;39(10):1776–84.

CAS  PubMed  PubMed Central  Google Scholar 

Ingalhalikar M, Shinde S, Karmarkar A, Rajan A, Rangaprakash D, Deshpande G. Functional connectivity-based prediction of autism on site harmonized ABIDE dataset. IEEE Trans Biomed Eng. 2021;68(12):3628–37.

PubMed  PubMed Central  Google Scholar 

Saponaro S, Giuliano A, Bellotti R, Lombardi A, Tangaro S, Oliva P, Retico A. Multi-site harmonization of MRI data uncovers machine-learning discrimination capability in barely separable populations: an example from the ABIDE dataset. NeuroImage: Clin. 2022;35:103082.

PubMed  Google Scholar 

Ecker C. The neuroanatomy of autism spectrum disorder: an overview of structural neuroimaging findings and their translatability to the clinical setting. Autism. 2017;21(1):18–28.

PubMed  Google Scholar 

Pomponio R, Erus G, Habes M, Doshi J, Srinivasan D, Mamourian E, Davatzikos C. Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan. NeuroImage. 2020;208:116450.

PubMed  Google Scholar 

Sussman D, Leung RC, Vogan VM, Lee W, Trelle S, Lin S, Taylor MJ. The autism puzzle: diffuse but not pervasive neuroanatomical abnormalities in children with ASD. NeuroImage: Clin. 2015;8:170–9.

CAS  PubMed  Google Scholar 

Haar S, Berman S, Behrmann M, Dinstein I. Anatomical abnormalities in autism? Cereb Cortex. 2016;26(4):1440–52.

PubMed  Google Scholar 

Ameis SH, Catani M. Altered white matter connectivity as a neural substrate for social impairment in autism spectrum disorder. Cortex. 2015;62:158–81.

PubMed  Google Scholar 

Retico A, Giuliano A, Tancredi R, Cosenza A, Apicella F, Narzisi A, Calderoni S. The effect of gender on the neuroanatomy of children with autism spectrum disorders: a support vector machine case-control study. Mol Autism. 2016;7:1–20.

Google Scholar 

Wolff JJ, Jacob S, Elison JT. The journey to autism: Insights from neuroimaging studies of infants and toddlers. Dev Psychopathol. 2018;30(2):479–95.

PubMed  Google Scholar 

Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosc. 2009;10(3):186–98.

CAS  Google Scholar 

Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52(3):1059–69.

PubMed  Google Scholar 

Sporns O. Structure and function of complex brain networks. Dialogues Clin Neurosci. 2013;15(3):247–62.

PubMed  PubMed Central  Google Scholar 

Qin J, Chen SG, Hu D, Zeng LL, Fan YM, Chen XP, Shen H. Predicting individual brain maturity using dynamic functional connectivity. Front Hum Neurosci. 2015;9:418.

PubMed  PubMed Central  Google Scholar 

De Domenico M, Sasai S, Arenas A. Mapping multiplex hubs in human functional brain networks. Front Neurosci. 2016;10:205256.

Google Scholar 

Rudie JD, Brown JA, Beck-Pancer D, Hernandez LM, Dennis EL, Thompson PM, Dapretto MJNC. Altered functional and structural brain network organization in autism. NeuroImage Clin. 2013;2:79–94.

Google Scholar 

Lewis JD, Evans AC, Pruett JR, Botteron K, Zwaigenbaum L, Estes A, Piven J. Network inefficiencies in autism spectrum disorder at 24 months. Transl Psychiatry. 2014;4(5):e388.

CAS  PubMed  PubMed Central  Google Scholar 

Amoroso N, La Rocca M, Bellantuono L, Diacono D, Fanizzi A, Lella E, Bellotti R. Deep learning and multiplex networks for accurate modeling of brain age. Front Aging Neurosci. 2019;11:115.

PubMed  PubMed Central  Google Scholar 

Bellantuono L, Marzano L, La Rocca M, Duncan D, Lombardi A, Maggipinto T, Bellotti R. Predicting brain age with complex networks: from adolescence to adulthood. NeuroImage. 2021;225:117458.

PubMed  Google Scholar 

Nie P, Yang G, Wang Y, Xu Y, Yan L, Zhang M, Wang Z. A CT-based deep learning radiomics nomogram outperforms the existing prognostic models for outcome prediction in clear cell renal cell carcinoma: a multicenter study. Eur Radiol. 2023;33(12):8858–68.

PubMed  Google Scholar 

Nie P, Liu S, Zhou R, Li X, Zhi K, Wang Y, Yang G. A preoperative CT-based deep learning radiomics model in predicting the stage, size, grade and necrosis score and outcome in localized clear cell renal cell carcinoma: a multicenter study. Eur J Radiol. 2023;166:111018.

PubMed  Google Scholar 

Li X, Shi X, Wang Y, Pang J, Zhao X, Xu Y, Nie P. A CT-based radiomics nomogram for predicting histologic grade and outcome in chondrosarcoma. Cancer Imaging. 2024;24(1):50.

CAS  PubMed  PubMed Central  Google Scholar 

Balafar MA, Ramli AR, Saripan MI, Mashohor S. Review of brain MRI image segmentation methods. Artif Intell Rev. 2010;33:261–74.

Google Scholar 

Newman ME. The mathematics of networks. New Palgrave Encycl Economics. 2008;2(2008):1–12.

Google Scholar 

Latora V, Marchiori M. Efficient behavior of small-world networks. Phys Rev Lett. 2001;87(19):198701.

CAS  PubMed  Google Scholar 

Newman ME. Assortative mixing in networks. Phys Rev Lett. 2002;89(20):208701.

CAS  PubMed  Google Scholar 

Foster JG, Foster DV, Grassberger P, Paczuski M. Edge direction and the structure of networks. Proc Natl Acad Sci. 2010;107(24):10815–20.

CAS  PubMed  PubMed Central  Google Scholar 

Sparks BF, Friedman SD, Shaw DW, Aylward EH, Echelard D, Artru AA, Dager SR. Brain structural abnormalities in young children with autism spectrum disorder. Neurology. 2002;59(2):184–92.

CAS  PubMed  Google Scholar 

Kim SY, Choi US, Park SY, Oh SH, Yoon HW, Koh YJ, Lee CU. Abnormal activation of the social brain network in children with autism spectrum disorder: an FMRI study. Psychiatry Invest. 2014;12(1):37.

Google Scholar 

Yang D, George TG, Sobolewski CM, McMorrow SR, Pacheco C, King KT, Eggebrecht AT. Mapping brain function underlying naturalistic motor observation and imitation using high-density diffuse optical tomography. bioRxiv. 2025. https://doi.org/10.1101/2025.01.21.634109.

PubMed  PubMed Central  Google Scholar 

Igelström KM, Webb TW, Graziano MS. Functional connectivity between the temporoparietal cortex and cerebellum in autism spectrum disorder. Cerebral Cortex. 2017;27(4):2617–27.

PubMed  Google Scholar 

Mouga S, Duarte IC, Café C, Sousa D, Duque F, Oliveira G, Castelo-Branco M. Parahippocampal deactivation and hyperactivation of central executive, saliency and social cognition networks in autism spectrum disorder. J Neurodev Disord. 2022;14(1):9.

PubMed  PubMed Central  Google Scholar 

Zhi K, Wang Y, Yan L, Hou F, Wu J, Zhang S, Nie P. The interpretable CT-based vision transformer model for preoperative prediction of clear cell renal cell carcinoma SSIGN score and outcome. Insights Imaging. 2025;16(1):1–12.

Google Scholar 

Dhamale TD, Bhandari SU, Harpale VK, Nandan D. Autism spectrum disorder detection using parallel deep convolution neural network and generative adversarial networks. Traitement du Signal. 2024;41(2):643.

Google Scholar 

Liu X, Hasan MR, Gedeon T, Hossain MZ. MADE-for-ASD: a multi-atlas deep ensemble network for diagnosing autism spectrum disorder. Comput Biol Med. 2024;182:109083.

PubMed  Google Scholar 

Ma R, Xie R, Wang Y, Meng J, Wei Y, Cai Y, Pan Y. Autism spectrum disorder classification with interpretability in children based on structural MRI features extracted using contrastive variational autoencoder. Big Data Min Anal. 2024;7(3):781–93.

Google Scholar 

Nogay HS, Adeli H. Multiple classification of brain MRI autism spectrum disorder by age and gender using deep learning. J Med Syst. 2024;48(1):15.

PubMed