Abstract
The neuro imaging developmental classification studies are undergone with small amount of samples from the brain activity samples. It promises the inspiring complications in high dimensional data analysis. Autism prediction methodologies are based on behavioral function alone previously which provides good precision but repossession will be unfortunate. We address those problems for early prediction of autism with neural development modern techniques and compared with older. Moreover, visualization of brain activities is quite important in neuro imaging. We believe in better visualization and classification of neuro images in early month captures and appended of Mullen Scales of Early Learning (MSEL). Functional magnetic resonance imaging (fMRI) is one of the controlling tools for measuring non-invasively measure brain activity and it provides with good resolution. For high resolution of brain activity, fMRI gives better than electro encephalon graph (EEG). Visualization of brain activity very clearly is first step to recognize the faults of autism. We have taken into the account for predicting in early Autism Spectrum Disorder (ASD) with help of multiple behavioral activities and development measures using machine learning algorithm. The prediction methods are examined with mostly many prediction methods start to examine the neuro imaging with ultra-high risk factors. The prediction of ASD is moderate accuracy in 14 month development measures from multiple time points. In this proposed work, Mullen early prediction is appended for early prediction and it is examined with computational approach to fMRI analysis with adaptive functioning classifier for machine learning algorithm. This proposed algorithm provides improved version of classification in machine languages with MSEL and high accuracy with conservative methods.
References
Sarraf, Saman & Sun, Jian. “Advances in Functional Brain Imaging: A Comprehensive Survey for Engineers and Physical Scientists” International Journal of Advanced Research. 4. 640-660. 10.21474/IJAR01/1259. (2016).
Webb SJ, Jones EJ, Kelly J, Dawson G. “The motivation for very early intervention for infants at high risk for autism spectrum disorders”. Int J Speech Lang Pathol. 2014 Feb;16(1):36-42. doi: 10.3109/17549507.2013.861018. PMID: 24410019; PMCID: PMC4141560.
Suman Raj “Analysis and Detection of Autism Spectrum Disorder Using Machine Learning Techniques” Published in Procedia Computer Science 167:994-1004, January 2020, DOI: 10.1016/j.procs.2020.03.399
Lemoine, Lise & Schneider, Benoît, “Autism Spectrum Disorder in French children's literature: an analysis of portrayals of children with autism in the light of the DSM-5” Research in Autism Spectrum Disorders. 80. 10.1016/j.rasd.2020.101675. (2020).
Carrie Cuffman, Kimberly Burkhart, “Constipation prevalence and perceptions: Comparison of children and adolescents with ASD and other developmental-behavioral disorders” Research in Autism Spectrum Disorders, Volume 80, 2021, https://doi.org/10.1016/j.rasd.2020.101710.
Vaishali, R., and R. Sasikala. "A machine learning based approach to classify Autism with optimum behaviour sets” (2018) International Journal of Engineering & Technology 7(4): 18.
Fadi Thabtah. (2017). “Autism spectrum disorder screening: machine learning adaptation and DSM-5 fulfillment.” In Proceedings of the 1st International Conference on Medical and Health Informatics, pp. 1-6. ACM.
M. S. Mythili, and AR Mohamed Shanavas. “A study on Autism spectrum disorders using classification techniques”, International Journal of Soft Computing and Engineering (IJSCE), 4: 88-91, (2014)
J. A. Kosmicki, V. Sochat, M. Duda, and D. P. Wall. (2015) “Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning”, Translational psychiatry, 5(2): e514.
Baihua Li, Arjun Sharma, James Meng, Senthil Purushwalkam, and Emma Gowen. (2017) “Applying machine learning to identify autistic adults using imitation: An exploratory study.” PloS one, 12(8): e0182652.
Arbabshirani, M. R., Plis, S., Sui, J. & Calhoun, V. D. “Single subject prediction of brain disorders in neuroimaging: promises and pitfalls”, Neuro Image 145, 137–165 (2017).
Bi, Xia-an & Jiang, Qin & Sun, Qi & Shu, Qing & Liu, Yingchao.. “Analysis of Alzheimer’s Disease Based on the Random Neural Network Cluster in fMRI” Frontiers in Neuroinformatics. 12. 10.3389/fninf.2018.00060. (2018)
Cohen, Jonathan & Daw, Nathaniel & Engelhardt, Barbara & Hasson, Uri & Li, Kai & Niv, Yael & Norman, Kenneth & Pillow, Jonathan & Ramadge, Peter & Turk-Browne, Nick & Willke, Theodore. “Computational approaches to fMRI analysis” Nature Neuroscience. 20. 304. 10.1038/nn.4499.(2017)
Eklund A, Andersson M, Knutsson H. “fMRI analysis on the GPU-possibilities and challenges” Comput Methods Programs Biomed. 2012 Feb;105(2):145-61. doi: 10.1016/j.cmpb.2011.07.007. Epub 2011 Aug 20. PMID: 21862169.
Bi XA, Shu Q, Sun Q, Xu Q. “Random support vector machine cluster analysis of resting-state fMRI in Alzheimer's disease” PLoS One. 2018 Mar 23;13(3):e0194479. doi: 10.1371/journal.pone.0194479. PMID: 29570705; PMCID: PMC5865739.
Libero LE, DeRamus TP, Lahti AC, Deshpande G, Kana RK. “Multimodal neuroimaging based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlates” Cortex. 2015 May;66:46-59. doi: 10.1016/j.cortex.2015.02.008. Epub 2015 Mar 3. PMID: 25797658; PMCID: PMC4782775.
Murdaugh DL, Shinkareva SV, Deshpande HR, Wang J, Pennick MR, Kana RK. “Differential deactivation during mentalizing and classification of autism based on default mode network connectivity” PLoS One. 2012;7(11):e50064. doi: 10.1371/journal.pone.0050064. Epub 2012 Nov 19. PMID: 23185536; PMCID: PMC3501481.
Bussu G, Jones EJH, Charman T, Johnson MH, Buitelaar JK; BASIS Team. “Prediction of Autism at 3 Years from Behavioural and Developmental Measures in High-Risk Infants: A Longitudinal Cross-Domain Classifier Analysis” J Autism Dev Disord. 2018 Jul;48(7):2418-2433. doi: 10.1007/s10803-018-3509-x. PMID: 29453709; PMCID: PMC5996007.
Deshpande G, Libero LE, Sreenivasan KR, Deshpande HD, Kana RK. “Identification of neural connectivity signatures of autism using machine learning” Front Hum Neurosci. 2013 Oct 17;7:670. doi: 10.3389/fnhum.2013.00670. PMID: 24151458; PMCID: PMC3798048.
Christensen DL, Bilder DA, Zahorodny W, Pettygrove S, Durkin MS, Fitzgerald RT, Rice C, Kurzius-Spencer M, Baio J, Yeargin-Allsopp M. “Prevalence and Characteristics of Autism Spectrum Disorder Among 4-Year-Old Children in the Autism and Developmental Disabilities Monitoring Network” J Dev Behav Pediatr. 2016 Jan;37(1):1-8. doi: 10.1097/DBP.0000000000000235. PMID: 26651088.
Bi XA, Shu Q, Sun Q, Xu Q. “Random support vector machine cluster analysis of resting-state fMRI in Alzheimer's disease” PLoS One. 2018 Mar 23;13(3):e0194479. doi: 10.1371/journal.pone.0194479. PMID: 29570705; PMCID: PMC5865739.
Siobhan O’Hagan, Caroline Bond, Judith Hebron,”What do we know about home education and autism? A thematic synthesis review”, Research in Autism Spectrum Disorders, Volume 80,2021,101711, ISSN 1750-9467, https://doi.org/10.1016/j.rasd.2020.101711.
Greenstein D, Malley JD, Weisinger B, Clasen L, Gogtay N. “Using multivariate machine learning methods and structural MRI to classify childhood onset schizophrenia and healthy controls” Front Psychiatry. 2012 Jun 1;3:53. doi: 10.3389/fpsyt.2012.00053. PMID: 22675310; PMCID: PMC3365783.
May, Tamara & Yi, Karen & Loveland, Kate & Vollenhoven, Beverley & Williams, Katrina. (2021). “Overlap of autism and conditions associated with atypical sex hormone levels or response: A systematic review and meta-analysis”, Research in Autism Spectrum Disorders. 80. 101693. 10.1016/j.rasd.2020.101693.
Xu L, Geng X, He X, Li J, Yu J. “Prediction in Autism by Deep Learning Short-Time Spontaneous Hemodynamic Fluctuations” Front Neurosci. 2019 Nov 8;13:1120. doi: 10.3389/fnins.2019.01120. PMID: 31780879; PMCID: PMC6856557.
Werkman, M.F. & Brouwer, Sandra & Dijkxhoorn, Y.M. & Berckelaer-Onnes, I.A. & Reijneveld, S.A. & Landsman, J.A. & Begeer, Sander. (2020) “The moderating effect of cognitive abilities on the association between sensory processing and emotional and behavioural problems and social participation in autistic individuals”, Research in Autism Spectrum Disorders. 78. 101663. 10.1016/j.rasd.2020.101663.
Yousef M, Jung S, Showe LC, Showe MK. “Recursive cluster elimination (RCE) for classification and feature selection from gene expression data” BMC Bioinformatics. 2007 May 2;8:144. doi: 10.1186/1471-2105-8-144. PMID: 17474999; PMCID: PMC1877816.
El-Baz, Ayman. “Machine learning applications to recognize autism and Alzheimer’s disease” 10.1088/978-0-7503-1793-1ch1,(2019).
assin, W., Nakatani, H., Zhu, Y. et al. “Machine-learning classification using neuroimaging data in schizophrenia, autism, ultra-high risk and first-episode psychosis” Transl Psychiatry 10, 278 (2020). https://doi.org/10.1038/s41398-020-00965-5
Rudovic, Ognjen & Lee, Jaeryoung & Dai, Miles & Schuller, Björn & Picard, Rosalind. (2018). “Personalized Machine Learning for Robot Perception of Affect and Engagement in Autism Therapy” Science. 3. 10.1126/scirobotics.aao6760.
Spain, Debbie & Sin, Jacqueline & Linder, Kai & McMahon, Johanna & Happe, Francesca. (2018). “Social anxiety in autism spectrum disorder: A systematic review”, Research in Autism Spectrum Disorders. 52. 51-68. 10.1016/j.rasd.2018.04.007.
Mateos-Pérez, José & Dadar, Mahsa & Lacalle-Aurioles, María & Iturria, Yasser & Zeighami, Yashar & Evans, Alan. “Structural neuroimaging as clinical predictor: a review of machine learning applications.” (2018)
Emily McDougal, Deborah M. Riby, Mary Hanley, “Teacher insights into the barriers and facilitators of learning in autism, Research in Autism Spectrum Disorders”, Volume 79, 2020, 101674, ISSN 1750-9467, https://doi.org/10.1016/j.rasd.2020.101674.
Ismail MM, Keynton RS, Mostapha MM, ElTanboly AH, Casanova MF, Gimel'farb GL, El-Baz A. “Studying Autism Spectrum Disorder with Structural and Diffusion Magnetic Resonance Imaging: A Survey”, Front Hum Neurosci. 2016 May 11;10:211. doi: 10.3389/fnhum.2016.00211. PMID: 27242476; PMCID: PMC4862981.
Webb SJ, Jones EJ, Kelly J, Dawson G. “The motivation for very early intervention for infants at high risk for autism spectrum disorders”. Int J Speech Lang Pathol. 2014 Feb;16(1):36-42. doi: 10.3109/17549507.2013.861018. PMID: 24410019; PMCID: PMC4141560.
Chaitra, N. & Vijaya, P.A. & Deshpande, Gopikrishna. “Diagnostic prediction of autism spectrum disorder using complex network measures in a machine learning framework” Biomedical Signal Processing and Control. 62. 102099. 10.1016/j.bspc.2020.102099. (2020)