Abstract
Autism spectrum disorder (ASD) is theoretically characterized by alterations in functional connectivity between brain regions. Many works presented approaches to determine informative patterns that help to predict autism from typical development. However, most of the proposed pipelines are not specifically designed for the autism problem, i.e. they do not corroborate with autism theories about functional connectivity. In this paper, we propose a framework that takes into account the properties of local connectivity and long range under-connectivity in the autistic brain. The originality of the proposed approach is to adopt elimination as a technique in order to well emerge the autistic brain connectivity alterations, and show how they contribute to differentiate ASD from controls. Experimental results conducted on the large multi-site Autism Brain Imaging Data Exchange (ABIDE) show that our approach provides accurate prediction up to 70% and succeeds to prove the existence of deficits in the long-range connectivity in the ASD subjects brains.
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The resting state fMRI images data used to support the findings of this study are from the ABIDE I dataset that have been cited. The preprocessed version of the data is available at: http://preprocessed-connectomes-project.org/abide/Pipelines.html.
References
Abraham A, Pedregosa F, Eickenberg M, Gervais P, Mueller A, Kossaifi J, Gramfort A, Thirion B, Varoquaux G (2014) Machine learning for neuroimaging with scikit-learn. Front Neuroinf 8:14. https://doi.org/10.3389/fninf.2014.00014
Abraham A, Milham MP, Di Martino A, Craddock RC, Samaras D, Thirion B, Varoquaux G (2017) Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example, NeuroImage, Volume 147, Pages 736-745, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2016.10.045
Alessandro G, Roma S, Remo J (2017) Editorial: advanced neuroimaging methods for studying autism disorder. Front Neurosci 11:533. https://doi.org/10.3389/fnins.2017.00533
Anjomshoa A, Dolatshahi M, Amirkhani F, Rahmani F, Mirbagheri M, Aarabi M (2016) Structural brain network analysis in schizophrenia using minimum spanning tree. vol 2016, pp 4075–4078. https://doi.org/10.1109/EMBC.2016.7591622
Baratloo A, Hosseini M, Negida A, El Ashal G (2015) Evidence-based emergency medicine; part 1: simple definition and calculation of accuracy, sensitivity and specificity. Emergency (Tehran, Iran) 3:48–49
Benabdallah F, Drissi El Malinai A, Lotfi D, Jennane R, El Hassouni M (2018) Analysis of under-connectivity in autism using the minimum spanning tree: application on large multi-site dataset. pp 296–299, https://doi.org/10.1109/ISIVC.2018.8709213
Blumensath T, Jbabdi S, Glasser M, Essen D, Ugurbil K, Behrens T, Smith S (2013) Spatially constrained hierarchical parcellation of the brain with resting-state fMRI. NeuroImage. https://doi.org/10.1016/j.neuroimage.2013.03.024
Bolte S, Girdler S, Marschik PB (2019) The contribution of environmental exposure to the etiology of autism spectrum disorder. Cell Mol Life Sci CMLS 76(7):1275–1297
Button K, Ioannidis J, Mokrysz C, Nosek B, Flint J, Robinson E, Munafò M (2013) Power failure: why small sample size undermines the reliability of neuroscience. Nature Rev Neurosci. https://doi.org/10.1038/nrn3475
Camprodon J, Stern T (2013) Selecting neuroimaging techniques: A review for the clinician. The primary care companion to CNS disorders 15. https://doi.org/10.4088/PCC.12f01490
Chang J, Gilman S, Chiang A, Sanders S, Vitkup D (2014) Genotype to phenotype relationships in autism spectrum disorders. Nature Neurosci. https://doi.org/10.1038/nn.3907
Courchesne E, Mouton PR, Calhoun ME, Semendeferi K, Ahrens-Barbeau C, Hallet MJ, Barnes CC, Pierce KJ (2011) Neuron number and size in prefrontal cortex of children with autism. JAMA 306(18):2001–2010
Craddock C, Benhajali Y, Carlton C, Francois C, Evans A, Jakab A, Khundrakpam B, Lewis J, Qingyang I, Michael M, Chaogan Y, Bellec P (2013) The neuro bureau preprocessing initiative: open sharing of preprocessed neuroimaging data and derivatives. Front Neuroinf. https://doi.org/10.3389/conf.fninf.2013.09.00041
Craddock C, Sharad S, Brian C, Ranjeet K, Satrajit G, Chaogan Y, Qingyang I, Daniel L, Vogelstein J, Burns R, Stanley C, Mennes M, Clare K, Adriana D, Castellanos F, Michael M (2013) Towards automated analysis of connectomes: the configurable pipeline for the analysis of connectomes (c-pac). Front Neuroinf. https://doi.org/10.3389/conf.fninf.2013.09.00042
Daintith J, Edmund W (2008) A dictionary of computing. Oxford University Press, Oxford. https://doi.org/10.1093/acref/9780199234004.001.0001
Du Y, Fu Z, Calhoun VD (2018) Classification and prediction of brain disorders using functional connectivity: promising but challenging. Front Neurosci 12:525. https://doi.org/10.3389/fnins.2018.00525
Emma F, Rhonda M, Teresa P, Alan FS (2021) Autism spectrum disorder: investigating predictive adaptive behavior skill deficits in young children, Autism research and treatment, vol. 2021, Article ID 8870461, 9 pages, https://doi.org/10.1155/2021/8870461
Esztergár-Kiss D, Caesar B (2017) Definition of user groups applying ward’s method. Transp Res Procedia 22:25–34. https://doi.org/10.1016/j.trpro.2017.03.004
Fang Z (2021) The methods and tools for clustering analysis. In: Wolkenhauer O (eds), Systems medicine, Academic Press, Pages 9-13, ISBN 9780128160787, https://doi.org/10.1016/B978-0-12-801238-3.11463-1
Ghanbari Y, Bloy L, Tunc B, Shankar V, Roberts T, Edgar J, Schultz R, Verma R (2015) On characterizing population commonalities and subject variations in brain networks. Med Image Anal. https://doi.org/10.1016/j.media.2015.10.009
Greicius M (2008) Resting-state functional connectivity in neuropsychiatric disorders. Current Opin Neurol 21:424–430. https://doi.org/10.1097/WCO.0b013e328306f2c5
Guo H, Liu L, Chen J, Xu Y, Jie X (2017) Alzheimer classification using a minimum spanning tree of high-order functional network on fMRI dataset. Front Neurosci 11:639. https://doi.org/10.3389/fnins.2017.00639
Harris JC (2016) Journal of the American Academy of Child & Adolescent Psychiatry 55(8):729 – 735. See http://www.sciencedirect.com/science/article/pii/S0890856716302805
Hazewinkel M (2013) Encyclopaedia of Mathematics: Volume 10. Encyclopaedia of Mathematics, Springer Netherlands. see https://books.google.co.ma/books?id=_YPtCAAAQBAJ
Hiremath CS, Sagar KJV, Yamini BK et al (2021) Emerging behavioral and neuroimaging biomarkers for early and accurate characterization of autism spectrum disorders: a systematic review. Transl Psychiatry 11:42. https://doi.org/10.1038/s41398-020-01178-6
Horowitz E, Sahni E, Sahni S (1978) Fundamentals of computer algorithms. xiv, 626 p : ill Potomac, Maryland: Computer Science Press Inc, include bibliography and index. 2019
Hull JV, Dokovna LB, Jacokes ZJ, Torgerson CM, Irimia A, Van Horn JD (2017) Resting-state functional connectivity in autism spectrum disorders: a review. Front Psychiatr 7:205. https://doi.org/10.3389/fpsyt.2016.00205
Keown CL, Shih P, Nair A, Peterson N, Mulvey ME, Müller RA (2013) Local functional overconnectivity in posterior brain regions is associated with symptom severity in autism spectrum disorders. Cell Rep 5(3):567–572. https://doi.org/10.1016/j.celrep.2013.10.003
Kundu P, Voon V, Balchandani P, Lombardo M, Poser B, Bandettini P (2017) Multi-echo fmri: A review of applications in fmri denoising and analysis of bold signals. NeuroImage. https://doi.org/10.1016/j.neuroimage.2017.03.033
Lau WKW, Leung MK, Lau BWM (2019) Resting-state abnormalities in autism spectrum disorders: a meta-analysis. Sci Rep 9:1–8
Li W, van Zijl PC (2020) Quantitative theory for the transverse relaxation time of blood water. NMR Biomed 33(5):427. https://doi.org/10.1002/nbm.4207
Li X, Jing Z, hu B, Jing Z, Zhong N, Li M, Ding Z, Yang J, Zhang L, Feng L, Majoe D (2017) A resting-state brain functional network study in MDD based on minimum spanning tree analysis and the hierarchical clustering. Complexity 1–11:2017. https://doi.org/10.1155/2017/9514369
Liu C, Xue J, Cheng X, Zhan W, Xiong X, Wang B (2019) Tracking the brain state transition process of dynamic function connectivity based on resting state fMRI”, Computational Intelligence and Neuroscience, vol. 2019, Article ID 9027803, 12 pages. https://doi.org/10.1155/2019/9027803
Maenner MJ, Shaw KA, Baio J, et al (2016) Prevalence of Autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States. MMWR Surveill Summ 2020;69(No. SS-4):1–12. https://doi.org/10.15585/mmwr.ss6904a1
Meijie L, Baojuan L, Dewen H (2021) Autism spectrum disorder studies using fMRI data and machine learning: a review. Front Neurosci 15:1111. https://doi.org/10.3389/fnins.2021.697870
Mumford J (2012) A power calculation guide for fMRI studies. Soc Cognit Affect Neurosci 7:738–742. https://doi.org/10.1093/scan/nss059
Nielsen J, Zielinski B, Fletcher P, Alexander A, Lange N, Bigler E, Lainhart J, Anderson J (2013) Multisite functional connectivity MRI classification of autism: Abide results. Front Human Neurosci 7:599. https://doi.org/10.3389/fnhum.2013.00599
Nierhaus T, Margulies D, Long X, Villringer A (2012) fMRI for the assessment of functional connectivity. https://doi.org/10.5772/23864
Oliphant T (2007) Scipy: Open source scientific tools for python 9:10–20
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E, Louppe G (2012) Scikit-learn: Machine learning in python. J Mach Learn Res 12
Salomone E, Settanni M, Ferrara F, Salandin A (2019) CST Italy team. The interplay of communication skills, emotional and Behavioural problems and parental psychological Distress. J Autism Dev Disord 49(11):4365–4374. https://doi.org/10.1007/s10803-019-04142-6
Stiles J, Jernigan TL (2010) The basics of brain development. Neuropsychol Rev 20(4):327–348. https://doi.org/10.1007/s11065-010-9148-4
Strother S (2006) Evaluating fMRI preprocessing pipelines. IEEE Eng Med Biol Mag Q Mag Eng Med Biol Soc 25:27–41. https://doi.org/10.1109/MEMB.2006.1607667
Supekar K, Uddin LQ, Khouzam A, Phillips J, Gaillard WD, Kenworthy LE, Yerys BE, Vaidya CJ, Menon V (2013) Brain hyperconnectivity in children with autism and its links to social deficits. Cell Rep 5(3):738–747. https://doi.org/10.1016/j.celrep.2013.10.001
Tang XY, Zeng WM, Wang N, Shi YH, Zhao L (2017) A novel layered data reduction mechanism for clustering fMRI data. Biomed Signal Process Control. https://doi.org/10.1016/j.bspc.2016.11.014
Tzourio-Mazoyer N, Landeau B, DF P, Crivello F, Etard O, Delcroix N, Mazoyer B, Marc J (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15:273–289. https://doi.org/10.1006/nimg.2001.0978
Varoquaux G, Gramfort A, Pedregosa F, Michel V, Thirion B (2011) Multi-subject dictionary learning to segment an atlas of brain spontaneous activity. vol-22, pp 562–73
Yang J, Zhong N, Liang P, Wang J, Yao Y, Lu S (2007) Brain activation detection by neighborhood one-class SVM. Cognit Syst Res 11:16–24. https://doi.org/10.1016/j.cogsys.2008.08.001
Zafar R, Malik A, Kamel N, Dass S (2016) Role of voxel selection and roi in fmri data analysis. pp 1–6. https://doi.org/10.1109/MeMeA.2016.7533739
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Benabdallah, F.Z., Drissi El Maliani, A., Lotfi, D. et al. An autism spectrum disorder adaptive identification based on the Elimination of brain connections: a proof of long-range underconnectivity. Soft Comput 26, 4701–4711 (2022). https://doi.org/10.1007/s00500-022-06890-7
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DOI: https://doi.org/10.1007/s00500-022-06890-7