Autism Spectrum Disorder (ASD) is a pervasive disorder with a relatively high prevalence that manifests in early childhood. Magnetic Resonance Imaging (MRI) is currently not a screening tool for ASD and it is not used in the routine evaluation of the disorder unless other concerning clinical features are present. However, it plays an important role in ASD research and multiple advanced imaging and post-processing strategies are currently being employed to subgroup and to elucidate potential mechanisms of disease. When performing neuroimaging research in ASD it is important to keep the pronounced heterogeneity of the disorder in mind.
Autism Spectrum Disorder (ASD) is a disorder that manifests in early childhood. It has a relatively high prevalence, with the current estimated prevalence being approximately 1 in 68 children [1]. The prevalence has been increasing in recent decades, possibly due to increased recognition of the disorder, widened diagnostic criteria or a true increase in incidence [2]. The disease manifestations of ASD can be quite heterogeneous. Hallmark features are the early onset and pervasive nature of the disorder, impaired cognition, hypersensitivity to sensory stimuli, repetitive and restrictive behaviors and an insistence on sameness [3]. Language impairments and epilepsy are common clinical features as well.
The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) now combines several previously distinct diagnoses into the diagnosis of ASD [4]. These include autistic disorder, Asperger´s syndrome, childhood disintegrative disorder, and pervasive developmental disorder not otherwise specified. While this reflects some important behavioral manifestations of autism, it may amalgamate heterogeneous causes into a single category [5]. In addition, common causalities with other disorders such as early onset epilepsies, intellectual disability syndromes and brain malformations may not become as apparent [5]. In order to target potential therapeutic approaches and to guide research in the field of ASD, uniform neurocognitive and psychological assessment tools have been developed to differentiate autism form Asperger´s syndrome, non syndromic intellectual disability, pervasive developmental disorder not otherwise specified, childhood schizophrenia and other neurodevelopmental phenotypes [2].
The high degree of diversity within the category of ASD has been a major challenge in autism research, both in regard to finding potential (imaging-based and non-imaging-based) biomarkers and in regard to developing therapeutic concepts. Diagnostic criteria, biomarkers and therapeutic approaches may only be valid in subgroups and not necessarily in the entire population of patients with ASD and may therefore be not readily discerned. Currently, there is no single validated biomarker for ASD. Newer research approaches focus on the variability within the group and on large sample sizes [6].
While the role of imaging in patients with autism has been under debate for a long time, it is important to remember that there currently are no clear-cut diagnostic imaging features for ASD. Magnetic resonance imaging (MRI) is not used to diagnose ASD or to grade the severity of the disorder. Routine MRI is currently not recommended for patients with ASD, if they do not have focal neurological signs, epilepsy or dysmorphic features [7, 8]. In addition to these clinical features, neurodevelopmental regression, a suspected metabolic etiology or microcephaly usually warrant MRI [8]. MRI is also commonly used to screen for underlying conditions that can be associated with ASD such as tuberous sclerosis complex, neurofibromatosis type 1 or megalencephaly-associated disorders [2].
Various MR imaging features have been described on clinical MRI of patients with ASD, including ventriculomegaly, dilatation of the Virchow-Robin spaces, abnormal myelination, hypoplasia of the corpus callosum, arachnoid cysts, dilatation of the cisterna magna and hypoplasia of the inferior vermis; overall the prevalence seems to be similar to that of the general population, though [2, 9].
For research, however, MRI plays an important role, not only to exclude underlying brain malformations and syndromes, but also to group and subgroup patient populations and to potentially elucidate disease mechanisms. The high degree of heterogeneity and substantial complexity of the disorder calls for individualized approaches in neuroimaging research of ASD. To overcome considerations of limited sample size, potential overfitting and potential selection bias for smaller mono-institutional approaches, larger-scale, multi-institutional initiatives have been created. These include The Autism Brain Imaging Data Exchange (ABIDE) dataset with open data-sharing providing structural and resting-state functional MRI data sets with corresponding phenotypic information from 539 individuals with ASD and 573 age-matched typical controls (http://fcon_1000.projects.nitrc.org/indi/abide/; [10]) and the EU-AIMS Longitudinal European Autism Project (LEAP) with a longitudinal approach [11].
When analyzing and interpreting the data, it is important to keep the pronounced heterogeneity of ASD in mind. For morphometric analyses, for example, smaller studies with well-characterized datasets had different classification accuracies than larger, more heterogeneous studies [12-14]. Classification accuracy could be improved when information on autism severity, verbal IQ and age were integrated in the analysis of morphometric features on MRI analyses of the ABIDE dataset [14]. Multiple neuroimaging based research endeavors are currently on-going, integrating both advanced sequence methodology (e.g. resting state fMRI with connectivity analyses and diffusion tensor techniques) [15, 16], postprocessing techniques and methods of artificial intelligence [17, 18].
1. Sahin M, Sur M. Genes, circuits, and precision therapies for autism and related neurodevelopmental disorders. Science. 2015 Nov 20;350(6263)
2. Rollins NK. An Imaging Glimpse into the Autistic Brain. Radiology. 2018 Jan;286(1):227-228
3. Geschwind DH, State MW. Gene hunting in autism spectrum disorder: on the path to precision medicine. Lancet Neurol. 2015 Nov;14(11):1109-20
4. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5th edition. American Psychiatric Publishing, Arlington, VA; 2013
5. Berg AT, Dobyns WB. Progress in autism and related disorders of brain development. Lancet Neurol. 2015 Nov;14(11):1069-70
6. Oldehinkel M, Mennes M, Marquand A, Charman T, Tillmann J, Ecker C, Dell'Acqua F, Brandeis D, Banaschewski T, Baumeister S, Moessnang C, Baron-Cohen S, Holt R, Bölte S, Durston S, Kundu P, Lombardo MV, Spooren W, Loth E, Murphy DGM, Beckmann CF, Buitelaar JK; EU-AIMS LEAP group. Altered Connectivity Between Cerebellum, Visual, and Sensory-Motor Networks in Autism Spectrum Disorder: Results from the EU-AIMS Longitudinal European Autism Project. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019 Mar;4(3):260-270.
7. Filipek PA, Accardo PJ, Ashwal S, Baranek GT, Cook EH Jr, Dawson G, Gordon B, Gravel JS, Johnson CP, Kallen RJ, Levy SE, Minshew NJ, Ozonoff S, Prizant BM, Rapin I, Rogers SJ, Stone WL, Teplin SW, Tuchman RF, Volkmar FR. Practice parameter: screening and diagnosis of autism: report of the Quality Standards Subcommittee of the American Academy of Neurology and the Child Neurology Society. Neurology. 2000 Aug 22;55(4):468-79
8. Schaefer GB, Mendelsohn NJ; Professional Practice and Guidelines Committee. Clinical genetics evaluation in identifying the etiology of autism spectrum disorders: 2013 guideline revisions. Genet Med 2013;1-5(5):399–407.
9. Boddaert N, Zilbovicius M, Philipe A, Robel L, Bourgeois M, Barthélemy C, Seidenwurm D, Meresse I, Laurier L, Desguerre I, Bahi-Buisson N, Brunelle F, Munnich A, Samson Y, Mouren MC, Chabane N. MRI findings in 77 children with non-syndromic autistic disorder. PLoS One. 2009;4(2):e4415
10. Di Martino A, Yan CG, Li Q, Denio E, Castellanos FX, Alaerts K, Anderson JS, Assaf M, Bookheimer SY, Dapretto M, Deen B, Delmonte S, Dinstein I, Ertl-Wagner B, Fair DA, Gallagher L, Kennedy DP, Keown CL, Keysers C, Lainhart JE, Lord C, Luna B, Menon V, Minshew NJ, Monk CS, Mueller S, Müller RA, Nebel MB, Nigg JT, O'Hearn K, Pelphrey KA, Peltier SJ, Rudie JD, Sunaert S, Thioux M, Tyszka JM, Uddin LQ, Verhoeven JS, Wenderoth N, Wiggins JL, Mostofsky SH, Milham MP. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry. 2014 Jun;19(6):659-67
11. Loth E, Charman T, Mason L, Tillmann J, Jones EJH, Wooldridge C, Ahmad J, Auyeung B, Brogna C, Ambrosino S, Banaschewski T, Baron-Cohen S, Baumeister S, Beckmann C, Brammer M, Brandeis D, Bölte S, Bourgeron T, Bours C, de Bruijn Y, Chakrabarti B, Crawley D, Cornelissen I, Acqua FD, Dumas G, Durston S, Ecker C, Faulkner J, Frouin V, Garces P, Goyard D, Hayward H, Ham LM, Hipp J, Holt RJ, Johnson MH, Isaksson J, Kundu P, Lai MC, D'ardhuy XL, Lombardo MV, Lythgoe DJ, Mandl R, Meyer-Lindenberg A, Moessnang C, Mueller N, O'Dwyer L, Oldehinkel M, Oranje B, Pandina G, Persico AM, Ruigrok ANV, Ruggeri B, Sabet J, Sacco R, Cáceres ASJ, Simonoff E, Toro R, Tost H, Waldman J, Williams SCR, Zwiers MP, Spooren W, Murphy DGM, Buitelaar JK. The EU-AIMS Longitudinal European Autism Project (LEAP): design and methodologies to identify and validate stratification biomarkers for autism spectrum disorders. Mol Autism. 2017 Jun 23;8:24
12. Ecker C, Rocha-Rego V, Johnston P, Mourao-Miranda J, Marquand A, Daly EM, Brammer MJ, Murphy C, Murphy DG; MRC AIMS Consortium. Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. Neuroimage. 2010 Jan 1;49(1):44-56
13. Ecker C, Marquand A, Mourão-Miranda J, Johnston P, Daly EM, Brammer MJ, Maltezos S, Murphy CM, Robertson D, Williams SC, Murphy DG. Describing the brain in autism in five dimensions--magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach. J Neurosci. 2010 Aug 11;30(32):10612-23
14. Katuwal GJ, Baum SA, Cahill ND, Michael AM. Divide and Conquer: Sub-Grouping of ASD Improves ASD Detection Based on Brain Morphometry. PLoS One. 2016 Apr 11;11(4):e0153331
15. ldehinkel M, Mennes M, Marquand A, Charman T, Tillmann J, Ecker C, Dell'Acqua F, Brandeis D, Banaschewski T, Baumeister S, Moessnang C, Baron-Cohen S, Holt R, Bölte S, Durston S, Kundu P, Lombardo MV, Spooren W, Loth E, Murphy DGM, Beckmann CF, Buitelaar JK; EU-AIMS LEAP group. Altered Connectivity Between Cerebellum, Visual, and Sensory-Motor Networks in Autism Spectrum Disorder: Results from the EU-AIMS Longitudinal European Autism Project. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019 Mar;4(3):260-270
16. Cerliani L, Mennes M, Thomas RM, Di Martino A, Thioux M, Keysers C. Increased Functional Connectivity Between Subcortical and Cortical Resting-State Networks in Autism Spectrum Disorder. JAMA Psychiatry. 2015 Aug;72(8):767-77
17. Heinsfeld AS, Franco AR, Craddock RC, Buchweitz A, Meneguzzi F. Identification of autism spectrum disorder using deep learning and the ABIDE dataset. Neuroimage Clin. 2017 Aug 30;17:16-23
18. Zhang F, Savadjiev P, Cai W, Song Y, Rathi Y, Tunç B, Parker D, Kapur T, Schultz RT, Makris N, Verma R, O'Donnell LJ. Whole brain white matter connectivity analysis using machine learning: An application to autism. Neuroimage. 2018 May 15;172:826-837