Kabasawa H. MR Imaging in the 21st Century: Technical Innovation over the First Two Decades. Magn Reson Med Sci 2022; 21(1):71-82. DOI:10.2463/mrms.rev.2021-0011.
Google Scholar
Yousaf T, Dervenoulas G, Politis M. Advances in MRI Methodology. Int Rev Neurobiol 2018; 141:31-76. DOI:10.1016/bs.irn.2018.08.008.
Google Scholar
Alamri A, Aljadhai YI, Alrashed A, et al. Identifying Clinical Clues in Children With Global Developmental Delay / Intellectual Disability With Abnormal Brain Magnetic Resonance Imaging (MRI). J Child Neurol 2021; 36(6):432-439. DOI:10.1177/0883073820977330.
Google Scholar
Wang L, Wang B, Wu C, et al. Autism Spectrum Disorder: Neurodevelopmental Risk Factors, Biological Mechanism, and Precision Therapy. Int J Mol Sci 2023; 24(3):1819. DOI: 10.3390/ijms24031819.
Google Scholar
Hodges H, Fealko C, Soares N. Autism spectrum disorder: definition, epidemiology, causes, and clinical evaluation. Transl Pediatr 2020; 9(Supp.1):S55-S65. DOI:10.21037/tp.2019.09.09.
Google Scholar
Okoye C, Obialo-Ibeawuchi CM, Obajeun OA, et al. Early Diagnosis of Autism Spectrum Disorder: A Review and Analysis of the Risks and Benefits. Cureus 2023; 15(8):e43226. DOI:10.7759/cureus.43226.
Google Scholar
Wang J, He Y. Toward individualized connectomes of brain morphology. Trends Neurosci 2024; 47(2):106-119. DOI:10.1016/j.tins.2023.11.011.
Google Scholar
Rafiee F, Rezvani Habibabadi R, Motaghi M, et al. Brain MRI in Autism Spectrum Disorder: Narrative Review and Recent Advances. J Magn Reson Imaging 2022; 55(6):1613-1624. DOI:10.1002/jmri.27949.
Google Scholar
Avino TA, Barger N, Vargas MV, et al. Neuron numbers increase in the human amygdala from birth to adulthood, but not in autism. Proc Natl Acad Sci USA 2018; 115(14):3710-3715. DOI:10.1073/pnas.1801912115.
Google Scholar
Courchesne E, Campbell K, Solso S. Brain growth across the life span in autism: age-specific changes in anatomical pathology. Brain Res 2011; 1380:138-45. DOI:10.1016/j.brainres.2010.09.101.
Google Scholar
Courchesne E, Redcay E, Kennedy DP. The autistic brain: birth through adulthood. Curr Opin Neurol 2004; 17(4):489-96. DOI:10.1097/01.wco.0000137542.14610.b4.
Google Scholar
Schumann CM, Amaral DG. Stereological analysis of amygdala neuron number in autism. J Neurosci 2006; 26(29):7674-9. DOI:10.1523/JNEUROSCI.1285-06.2006.
Google Scholar
Hazlett HC, Poe M, Gerig G, et al. Magnetic resonance imaging and head circumference study of brain size in autism: birth through age 2 years. Arch Gen Psychiatry 2005; 62(12):1366-76. DOI:10.1001/archpsyc.62.12.1366.
Google Scholar
Redcay E, Courchesne E. Deviant functional magnetic resonance imaging patterns of brain activity to speech in 2-3-year-old children with autism spectrum disorder. Biol Psychiatry 2008; 64(7):589-98. DOI:10.1016/j.biopsych.2008.05.020.
Google Scholar
Amaral DG, Schumann CM, Nordahl CW. Neuroanatomy of autism. Trends Neurosci 2008; 31(3):137-45. DOI:10.1016/j.tins.2007.12.005.
Google Scholar
Ma R, Xie R, Wang Y, et al. Autism Spectrum Disorder Classification in Children based on Structural MRI Features Extracted using Contrastive Variational Autoencoder. arXiv preprint arXiv:2307.00976. 2023.
Google Scholar
Song J, Chen Y, Yao Y, et al. Combining Radiomics and Machine Learning Approaches for Objective ASD Diagnosis: Verifying White Matter Associations with ASD. arXiv preprint arXiv:2405.16248. 2024.
Google Scholar
Gkintoni E, Panagioti M, Vassilopoulos SP, Nikolaou G, Boutsinas B, Vantarakis A. Leveraging AI-Driven Neuroimaging Biomarkers for Early Detection and Social Function Prediction in Autism Spectrum Disorders: A Systematic Review. Healthcare (Basel) 2025; 13(15):1776. DOI:10.3390/healthcare13151776.
Google Scholar
Stanfield AC, McIntosh AM, Spencer MD, et al. Towards a neuroanatomy of autism: A systematic review and meta-analysis of structural magnetic resonance imaging studies. European Psychiatry 2008; 23(4):289-299. DOI:10.1016/j.eurpsy.2007.05.006.
Google Scholar
Aoki Y, Abe O, Nippashi Y, et al. Comparison of white matter integrity between autism spectrum disorder subjects and typically developing individuals: A meta-analysis of diffusion tensor imaging tractography studies. Molecular Autism 2013; 4(1),25. DOI:10.1186/2040-2392-4-25.
Google Scholar
Sundaram SK, Kumar A, Makki MI, et al. Diffusion tensor imaging of frontal lobe in autism spectrum disorder. Cereb Cortex 2008; 18(11):2659-2665. DOI:10.1093/cercor/bhn031.
Google Scholar
Han J, Zeng K, Kang J, et al. Development of Brain Network in Children with Autism from Early Childhood to Late Childhood. Neuroscience 2017; 367:134-146. DOI:10.1016/j.neuroscience.2017.10.015.
Google Scholar
Deshpande G, Libero LE, Sreenivasan KR, et al. Identification of neural connectivity signatures of autism using machine learning. Front Hum Neurosci 2013; 7:670. DOI:10.3389/fnhum.2013.00670.
Google Scholar
Liu M, Li B, Hu D. Autism Spectrum Disorder Studies Using fMRI Data and Machine Learning: A Review. Front Neurosci 2021; 15:697870. DOI:10.3389/fnins.2021.697870.
Google Scholar
Dalton KM, Nacewicz BM, Johnstone T, et al. Gaze fixation and the neural circuitry of face processing in autism. Nat Neurosci 2005; 8(4):519-26. DOI:10.1038/nn1421.
Google Scholar
Pelphrey KA, Morris JP, Michelich CR, et al. Functional anatomy of biological motion perception in posterior temporal cortex: an FMRI study of eye, mouth and hand movements. Cereb Cortex 2005; 15(12):1866-76. DOI:10.1093/cercor/bhi064.
Google Scholar
Schultz RT, Grelotti DJ, Klin A, et al. Abnormal activation of the social brain during face perception in autism. Hum Brain Mapp 2003; 19(3):131-9. DOI:10.1002/hbm.10123.
Google Scholar
Herrington JD, Nymberg C, Schultz RT. Biological motion task performance predicts superior temporal sulcus activity. Brain Cogn 2011; 77(3):372-81. DOI:10.1016/j.bandc.2011.08.010.
Google Scholar
Knaus TA, Silver AM, Lindgren KA, et al. fMRI activation during a language task in adolescents with ASD. J Int Neuropsychol Soc 2008; 14(6):967-79. DOI:10.1017/S1355617708081216.
Google Scholar
Just MA, Cherkassky VL, Keller TA, et al. Cortical activation and synchronization during sentence comprehension in high-functioning autism: evidence of underconnectivity. Brain 2004; 127(Pt 8):1811-21. DOI:10.1093/brain/awh199.
Google Scholar
Hazlett EA, Buchsbaum MS, Haznedar MM, et al. Regional glucose metabolism within cortical Brodmann areas in healthy individuals and autistic patients. Neuropsychobiology 2004; 49(2):115-126. DOI:10.1159/000076412.
Google Scholar
Chugani DC, Muzik O, Behen M, et al. Developmental changes in brain serotonin synthesis capacity in autistic and nonautistic children. Ann Neurol 1999; 45(3):287-295. DOI:10.1002/1531-8249(199903)45:3<287::AID-ANA3>3.0.CO;2-9.
Google Scholar
Suzuki K, Sugihara G, Ouchi Y, et al. Microglial activation in young adults with autism spectrum disorder: PET study using ¹¹C-PK11195. Brain 2013; 136(11):3426-3435. DOI:10.1093/brain/awt287.
Google Scholar
Keeratitanont K, Srikam C, Plengpanich W, et al. Brain laterality evaluated by F-18 fluorodeoxyglucose PET/CT in patients with high-functioning autism spectrum disorder. Front Mol Neurosci 2022; 15:901016. DOI:10.3389/fnmol.2022.901016.
Google Scholar
Zürcher NR, Bhanot A, McDougle CJ, Hooker JM. A systematic review of molecular imaging (PET and SPECT) in autism spectrum disorder. Neurosci Biobehav Rev 2015; 52:56-73. DOI:10.1016/j.neubiorev.2015.02.010.
Google Scholar
Fatemi SH, Folsom TD, Kneeland RE, et al. Metabotropic glutamate receptor 5 tracer [¹⁸F]-FPEB displays altered binding potential in autism. Cerebellum Ataxias 2018; 5:8. DOI: 10.1186/s40673-018-0089-5.
Google Scholar
Żarnowska I, Choińska A, Jeśko H, et al. A case report of clinical and 18FDG PET findings in autism: ketogenic diet as a therapeutic approach. Metab Brain Dis 2018; 33(4):1187-1192. DOI:10.1007/s11011-018-0233-0.
Google Scholar
https://doi.org/10.36553/wm.174
Język Polski
English
