Browse > Article
http://dx.doi.org/10.5765/jkacap.200021

Neuroimaging-Based Deep Learning in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder  

Song, Jae-Won (Department of Child and Adolescent Psychiatry, Seoul National University Hospital)
Yoon, Na-Rae (Department of Child and Adolescent Psychiatry, Seoul National University Hospital)
Jang, Soo-Min (Department of Child and Adolescent Psychiatry, Seoul National University Hospital)
Lee, Ga-Young (Seoul National University Hospital, Autism and Developmental Disorder Center)
Kim, Bung-Nyun (Department of Child and Adolescent Psychiatry, Seoul National University Hospital)
Publication Information
Journal of the Korean Academy of Child and Adolescent Psychiatry / v.31, no.3, 2020 , pp. 97-104 More about this Journal
Abstract
Deep learning (DL) is a kind of machine learning technique that uses artificial intelligence to identify the characteristics of given data and efficiently analyze large amounts of information to perform tasks such as classification and prediction. In the field of neuroimaging of neurodevelopmental disorders, various biomarkers for diagnosis, classification, prognosis prediction, and treatment response prediction have been examined; however, they have not been efficiently combined to produce meaningful results. DL can be applied to overcome these limitations and produce clinically helpful results. Here, we review studies that combine neurodevelopmental disorder neuroimaging and DL techniques to explore the strengths, limitations, and future directions of this research area.
Keywords
Neuroimaging; Neurodevelopmental disorder; Autism spectrum disorder; Attention-deficit/hyperactivity disorder; Deep learning; Review;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Harvey EA, Youngwirth SD, Thakar DA, Errazuriz PA. Predicting attention-deficit/hyperactivity disorder and oppositional defiant disorder from preschool diagnostic assessments. J Consult Clin Psychol 2009;77:349-354.   DOI
2 Tandon M, Si X, Luby J. Preschool onset attention-deficit/hyperactivity disorder: course and predictors of stability over 24 months. J Child Adolesc Psychopharmacol 2011;21:321-330.   DOI
3 Pierce EW, Ewing LJ, Campbell SB. Diagnostic status and symptomatic behavior of hard-to-manage preschool children in middle childhood and early adolescence. J Clin Child Psychol 1999;28:44-57.   DOI
4 McGee R, Partridge F, Williams S, Silva PA. A twelve-year followup of preschool hyperactive children. J Am Acad Child Adolesc Psychiatry 1991;30:224-232.   DOI
5 Vieira S, Pinaya WH, Mechelli A. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: methods and applications. Neurosci Biobehav Rev 2017;74(Pt A):58-75.   DOI
6 Kamal H, Lopez V, Sheth SA. Machine learning in acute ischemic stroke neuroimaging. Front Neurol 2018;9:945.   DOI
7 Mateos-Perez JM, Dadar M, Lacalle-Aurioles M, Iturria-Medina Y, Zeighami Y, Evans AC. Structural neuroimaging as clinical predictor: a review of machine learning applications. Neuroimage Clin 2018;20:506-522.   DOI
8 Feng R, Badgeley M, Mocco J, Oermann EK. Deep learning guided stroke management: a review of clinical applications. J Neurointerv Surg 2018;10:358-362.   DOI
9 Davatzikos C. Machine learning in neuroimaging: progress and challenges. Neuroimage 2019;197:652-656.   DOI
10 Zaharchuk G, Gong E, Wintermark M, Rubin D, Langlotz CP. Deep learning in neuroradiology. AJNR Am J Neuroradiol 2018;39:1776-1784.   DOI
11 Li G, Chen MH, Li G, Wu D, Lian C, Sun Q, et al. A longitudinal MRI study of amygdala and hippocampal subfields for infants with risk of autism. Graph Learn Med Imaging (2019) 2019;11849:164-171.   DOI
12 Zhu G, Jiang B, Tong L, Xie Y, Zaharchuk G, Wintermark M. Applications of deep learning to neuro-imaging techniques. Front Neurol 2019;10:869.   DOI
13 Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, et al. Deep learning: a primer for radiologists. Radiographics 2017;37:2113-2131.   DOI
14 Lanka P, Rangaprakash D, Dretsch MN, Katz JS, Denney TS Jr, Deshpande G. Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets. Brain Imaging Behav 2019 Nov 5 [Epub]. https://doi.org/10.1007/s11682-019-00191-8.
15 Moon SJ, Hwang J, Kana R, Torous J, Kim JW. Accuracy of machine learning algorithms for the diagnosis of autism spectrum disorder: systematic review and meta-analysis of brain magnetic resonance imaging studies. JMIR Ment Health 2019;6:e14108.   DOI
16 Li G, Liu M, Sun Q, Shen D, Wang L. Early diagnosis of autism disease by multi-channel CNNs. Mach Learn Med Imaging 2018;11046:303-309.   DOI
17 Yoo JH, Kim JI, Kim BN, Jeong B. Exploring characteristic features of attention-deficit/hyperactivity disorder: findings from multi-modal MRI and candidate genetic data. Brain Imaging Behav 2019 Jul 18 [Epub]. https://doi.org/10.1007/s11682-019-00164-x.
18 Moberget T, Alnaes D, Kaufmann T, Doan NT, Cordova-Palomera A, Norbom LB, et al. Cerebellar gray matter volume is associated with cognitive function and psychopathology in adolescence. Biol Psychiatry 2019;86:65-75.   DOI
19 Plis SM, Hjelm DR, Salakhutdinov R, Allen EA, Bockholt HJ, Long JD, et al. Deep learning for neuroimaging: a validation study. Front Neurosci 2014;8:229.
20 Sidhu G. Locally linear embedding and fMRI feature selection in psychiatric classification. IEEE J Transl Eng Health Med 2019;7:2200211.   DOI
21 Xiao Z, Wu J, Wang C, Jia N, Yang X. Computer-aided diagnosis of school-aged children with ASD using full frequency bands and enhanced SAE: a multi-institution study. Exp Ther Med 2019;17:4055-4063.
22 Chen H, Duan X, Liu F, Lu F, Ma X, Zhang Y, et al. Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity--a multi-center study. Prog Neuropsychopharmacol Biol Psychiatry 2016;64:1-9.   DOI
23 Uddin LQ, Supekar K, Lynch CJ, Khouzam A, Phillips J, Feinstein C, et al. Salience network-based classification and prediction of symptom severity in children with autism. JAMA Psychiatry 2013;70:869-879.   DOI
24 Anderson JS, Nielsen JA, Froehlich AL, DuBray MB, Druzgal TJ, Cariello AN, et al. Functional connectivity magnetic resonance imaging classification of autism. Brain 2011;134:3742-3754.   DOI
25 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:e50064.   DOI
26 Iidaka T. Resting state functional magnetic resonance imaging and neural network classified autism and control. Cortex 2015;63:55-67.   DOI
27 Aghdam MA, Sharifi A, Pedram MM. Diagnosis of autism spectrum disorders in young children based on resting-state functional magnetic resonance imaging data using convolutional neural networks. J Digit Imaging 2019;32:899-918.   DOI
28 The ADHD-200 Consortium. The ADHD-200 Consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front Syst Neurosci 2012;6:62.   DOI
29 Deshpande G, Wang P, Rangaprakash D, Wilamowski B. Fully connected cascade artificial neural network architecture for attention deficit hyperactivity disorder classification from functional magnetic resonance imaging data. IEEE Trans Cybern 2015;45:2668-2679.   DOI
30 Jung M, Tu Y, Park J, Jorgenson K, Lang C, Song W, et al. Surfacebased shared and distinct resting functional connectivity in attention-deficit hyperactivity disorder and autism spectrum disorder. Br J Psychiatry 2019;214:339-344.   DOI
31 Kuang D, Guo X, An X, Zhao Y, He L. Discrimination of ADHD based on fMRI data with deep belief network. Proceedings of 10th International Conference; 2014 Aug 3-6; Taiyuan, China. Cham, Switzerland: Springer;2014.
32 Hao AJ, He BL, Yin CH. Discrimination of ADHD children based on deep Bayesian network. Proceedings of 2015 IET International Conference on Biomedical Image and Signal Processing; 2015 Nov 19; Beijing, China.
33 Wang XH, Jiao Y, Li L. Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity. Sci Rep 2018;8:11789.   DOI
34 Zhang F, Roeyers H. Exploring brain functions in autism spectrum disorder: a systematic review on functional near-infrared spectroscopy (fNIRS) studies. Int J Psychophysiol 2019;137:41-53.   DOI
35 Baird G, Cass H, Slonims V. Diagnosis of autism. BMJ 2003;327:488-493.   DOI
36 Xu L, Liu Y, Yu J, Li X, Yu X, Cheng H, et al. Characterizing autism spectrum disorder by deep learning spontaneous brain activity from functional near-infrared spectroscopy. J Neurosci Methods 2020;331:108538.   DOI
37 Xu L, Geng X, He X, Li J, Yu J. Prediction in autism by deep learning short-time spontaneous hemodynamic fluctuations. Front Neurosci 2019;13:1120.   DOI
38 Yoo JH, Sharma V, Kim JW, McMakin DL, Hong SB, Zalesky A, et al. Prediction of sleep side effects following methylphenidate treatment in ADHD youth. Neuroimage Clin 2020;26:102030.   DOI
39 Lai MC, Lombardo MV, Baron-Cohen S. Autism. Lancet 2014;383:896-910.   DOI
40 Filipek PA, Accardo PJ, Baranek GT, Cook EH Jr, Dawson G, Gordon B, et al. The screening and diagnosis of autistic spectrum disorders. J Autism Dev Disord 1999;29:439-484.   DOI
41 Schnack HG, Kahn RS. Detecting neuroimaging biomarkers for psychiatric disorders: sample size matters. Front Psychiatry 2016;7:50.
42 Todd MT, Nystrom LE, Cohen JD. Confounds in multivariate pattern analysis: theory and rule representation case study. Neuroimage 2013;77:157-165.   DOI
43 Woolgar A, Golland P, Bode S. Coping with confounds in multivoxel pattern analysis: what should we do about reaction time differences? A comment on Todd, Nystrom & Cohen 2013. Neuroimage 2014;98:506-512.   DOI
44 Alizadeh S, Jamalabadi H, Schonauer M, Leibold C, Gais S. Decoding cognitive concepts from neuroimaging data using multivariate pattern analysis. Neuroimage 2017;159:449-458.   DOI
45 Kambeitz J, Kambeitz-Ilankovic L, Leucht S, Wood S, Davatzikos C, Malchow B, et al. Detecting neuroimaging biomarkers for schizophrenia: a meta-analysis of multivariate pattern recognition studies. Neuropsychopharmacology 2015;40:1742-1751.   DOI
46 Campbell SB. Behavior problems in preschool children: a review of recent research. J Child Psychol Psychiatry 1995;36:113-149.   DOI
47 Kadesjo C, Kadesjo B, Hagglof B, Gillberg C. ADHD in Swedish 3-to 7-year-old children. J Am Acad Child Adolesc Psychiatry 2001;40:1021-1028.   DOI
48 Pineda D, Ardila A, Rosselli M, Arias BE, Henao GC, Gomez LF, et al. Prevalence of attention-deficit/hyperactivity disorder symptoms in 4- to 17-year-old children in the general population. J Abnorm Child Psychol 1999;27:455-462.   DOI
49 Smidts DP, Oosterlaan J. How common are symptoms of ADHD in typically developing preschoolers? A study on prevalence rates and prenatal/demographic risk factors. Cortex 2007;43:710-717.   DOI
50 Kambeitz J, Cabral C, Sacchet MD, Gotlib IH, Zahn R, Serpa MH, et al. Detecting neuroimaging biomarkers for depression: a metaanalysis of multivariate pattern recognition studies. Biol Psychiatry 2017;82:330-338.   DOI
51 Librenza-Garcia D, Kotzian BJ, Yang J, Mwangi B, Cao B, Pereira Lima LN, et al. The impact of machine learning techniques in the study of bipolar disorder: a systematic review. Neurosci Biobehav Rev 2017;80:538-554.   DOI
52 Kolbak M, Lauria K, Lee I, Mohan S, Phan HP, Salisbury J. Regularization for deep learning. Deep Learning 2016;221-261.
53 Prechelt L. Early stopping - but when? In: Montavon G, Orr GB, Muller KR, editors. Neural networks: tricks of the trade. 2nd ed. Heidelberg: Springer Berlin Heidelberg;2012. p.53-67.
54 Calhoun VD, Adali T. Feature-based f usion of medical imaging data. IEEE Trans Inf Technol Biomed 2009;13:711-720.   DOI
55 Springenberg JT, Dosovitskiy A, Brox T, Riedmiller MA. Striving for simplicity: the all convolutional net [cited 2020 May 1]. Available from URL: https://arxiv.org/abs/1412.6806.
56 Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 2014;15:1929-1958.
57 Pesapane F, Volonte C, Codari M, Sardanelli F. Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States. Insights Imaging 2018;9:745-753.   DOI
58 Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry 2010;167:748-751.   DOI
59 Suk HI, Lee SW, Shen D; Alzheimer's Disease Neuroimaging Initiative. Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Struct Funct 2015;220:841-859.   DOI
60 Zeiler MD, Fergus R. Visualizing and understanding convolutional networks. Proceeding of 13th European Conference on Computer Vision; 2014 Sep 6-12; Zurich, Switzerland. Cham, Switzerland: Springer;2014.