Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey
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Taban Eslami | Fahad Almuqhim | Joseph S. Raiker | Fahad Saeed | F. Saeed | Taban Eslami | Fahad Almuqhim | J. Raiker
[1] Ayşe Demirhan,et al. The effect of feature selection on multivariate pattern analysis of structural brain MR images. , 2018, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.
[2] John D. Lusher,et al. High-Performance Correlation and Mapping Engine for rapid generating brain connectivity networks from big fMRI data , 2018, J. Comput. Sci..
[3] F Xavier Castellanos,et al. Annual Research Review: Discovery science strategies in studies of the pathophysiology of child and adolescent psychiatric disorders--promises and limitations. , 2016, Journal of child psychology and psychiatry, and allied disciplines.
[4] Daoqiang Zhang,et al. Network-based classification of ADHD patients using discriminative subnetwork selection and graph kernel PCA , 2016, Comput. Medical Imaging Graph..
[5] Guido A. van Wingen,et al. A Hybrid 3DCNN and 3DC-LSTM Based Model for 4D Spatio-Temporal fMRI Data: An ABIDE Autism Classification Study , 2019, OR/MLCN@MICCAI.
[6] Guang-Bin Huang,et al. Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions , 1998, IEEE Trans. Neural Networks.
[7] Christoph U. Lehmann,et al. Clinical Practice Guideline for the Diagnosis, Evaluation, and Treatment of Attention-Deficit/Hyperactivity Disorder in Children and Adolescents , 2019, Pediatrics.
[8] Huiguang He,et al. Classification of ADHD children through multimodal magnetic resonance imaging , 2012, Front. Syst. Neurosci..
[9] M. Gerstein,et al. The Development of a Practical Artificial Intelligence Tool for Diagnosing and Evaluating Autism Spectrum Disorder: Multicenter Study , 2020, JMIR medical informatics.
[10] Dimitris Samaras,et al. Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example , 2016, NeuroImage.
[11] Hu Lu,et al. Brain Functional Connectivity Augmentation Method for Mental Disease Classification with Generative Adversarial Network , 2019, PRCV.
[12] S. Vigneshwaran,et al. Autism Spectrum Disorder Detection Using Projection Based Learning Metacognitive RBF Network , 2013 .
[13] Lianghua He,et al. Discrimination of ADHD Based on fMRI Data with Deep Belief Network , 2014, ICIC.
[14] Jeffrey L. Gunter,et al. Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks , 2018, SASHIMI@MICCAI.
[15] Jared A. Nielsen,et al. Multisite functional connectivity MRI classification of autism: ABIDE results , 2013, Front. Hum. Neurosci..
[16] Mert R. Sabuncu,et al. 3D Convolutional Neural Networks for Classification of Functional Connectomes , 2018, DLMIA/ML-CDS@MICCAI.
[17] Fahad Saeed,et al. Similarity based classification of ADHD using singular value decomposition , 2018, CF.
[18] DeLiang Wang,et al. Unsupervised Learning: Foundations of Neural Computation , 2001, AI Mag..
[19] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[20] Dinggang Shen,et al. Multiple-Network Classification of Childhood Autism Using Functional Connectivity Dynamics , 2014, MICCAI.
[21] Satrajit S. Ghosh,et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments , 2016, Scientific Data.
[22] Juntang Zhuang,et al. Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI , 2018, MICCAI.
[23] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[24] Yun Jiao,et al. Predictive models of autism spectrum disorder based on brain regional cortical thickness , 2010, NeuroImage.
[25] Mir Mohsen Pedram,et al. Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network , 2018, Journal of Digital Imaging.
[26] Y. Zang,et al. Inconsistency in Abnormal Functional Connectivity Across Datasets of ADHD-200 in Children With Attention Deficit Hyperactivity Disorder , 2019, Front. Psychiatry.
[27] Z. Warren,et al. Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2016 , 2020, Morbidity and mortality weekly report. Surveillance summaries.
[28] Léon Bottou,et al. Stochastic Gradient Descent Tricks , 2012, Neural Networks: Tricks of the Trade.
[29] Anke Meyer-Bäse,et al. High Performance GP-Based Approach for fMRI Big Data Classification , 2017, PEARC.
[30] Swathi P. Iyer,et al. Subtyping attention-deficit/hyperactivity disorder using temperament dimensions: toward biologically based nosologic criteria. , 2014, JAMA psychiatry.
[31] Greta M. Massetti,et al. Evidence-Based Assessment of Attention Deficit Hyperactivity Disorder in Children and Adolescents , 2005, Journal of clinical child and adolescent psychology : the official journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53.
[32] Mubarak Shah,et al. ADHD classification using bag of words approach on network features , 2012, Medical Imaging.
[33] Nicha C. Dvornek,et al. Combining phenotypic and resting-state fMRI data for autism classification with recurrent neural networks , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[34] P. Laffey. Psychiatric therapy in Georgian Britain , 2003, Psychological Medicine.
[35] Lester Melie-García,et al. Studying the human brain anatomical network via diffusion-weighted MRI and Graph Theory , 2008, NeuroImage.
[36] Canhua Wang,et al. Identification of Autism Based on SVM-RFE and Stacked Sparse Auto-Encoder , 2019, IEEE Access.
[37] Dimitri Van De Ville,et al. Dynamic Functional Connectivity of Resting-State Spinal Cord fMRI Reveals Fine-Grained Intrinsic Architecture , 2020, Neuron.
[38] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[39] Rushil Anirudh,et al. Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification , 2017, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[40] Chunyan Miao,et al. 3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI , 2017, IEEE Access.
[41] M. Eisenberg,et al. Evaluating the Evidence For and Against the Overdiagnosis of ADHD , 2007, Journal of attention disorders.
[42] Sakib Mostafa,et al. Diagnosis of Autism Spectrum Disorder Based on Eigenvalues of Brain Networks , 2019, IEEE Access.
[43] Li Yi,et al. Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework , 2016, Autism research : official journal of the International Society for Autism Research.
[44] Fahad Saeed,et al. Fast-GPU-PCC: A GPU-Based Technique to Compute Pairwise Pearson’s Correlation Coefficients for Time Series Data—fMRI Study , 2018, High-throughput.
[45] Nathan D. Cahill,et al. The predictive power of structural MRI in Autism diagnosis , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[46] Lili He,et al. A Novel Transfer Learning Approach to Enhance Deep Neural Network Classification of Brain Functional Connectomes , 2018, Front. Neurosci..
[47] C. Nelson,et al. EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach , 2018, Scientific Reports.
[48] Arnau Oliver,et al. Improving the detection of autism spectrum disorder by combining structural and functional MRI information , 2020, NeuroImage: Clinical.
[49] João Ricardo Sato,et al. Abnormal Functional Resting-State Networks in ADHD: Graph Theory and Pattern Recognition Analysis of fMRI Data , 2014, BioMed research international.
[50] Jing Li,et al. Machine Learning Approaches for the Neuroimaging Study of Alzheimer's Disease , 2011, Computer.
[51] Eric A Youngstrom,et al. A primer on receiver operating characteristic analysis and diagnostic efficiency statistics for pediatric psychology: we are ready to ROC. , 2014, Journal of pediatric psychology.
[52] S. Bauer,et al. A survey of MRI-based medical image analysis for brain tumor studies , 2013, Physics in medicine and biology.
[53] Huafu Chen,et al. Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity—A multi-center study , 2016, Progress in Neuro-psychopharmacology and Biological Psychiatry.
[54] Amirali Kazeminejad,et al. Topological Properties of Resting-State fMRI Functional Networks Improve Machine Learning-Based Autism Classification , 2019, Front. Neurosci..
[55] Katherine E Henson,et al. Risk of Suicide After Cancer Diagnosis in England , 2018, JAMA psychiatry.
[56] A. Franco,et al. NeuroImage: Clinical , 2022 .
[57] Deepti R. Bathula,et al. Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD , 2012, Proceedings of the National Academy of Sciences.
[58] Jing Sui,et al. Machine learning in major depression: From classification to treatment outcome prediction , 2018, CNS neuroscience & therapeutics.
[59] Arne Frick. Upper Bounds on the Number of Hidden Nodes in Sugiyama's Algorithm , 1996, Graph Drawing.
[60] J. Pekar,et al. A method for making group inferences from functional MRI data using independent component analysis , 2001, Human brain mapping.
[61] Russell Greiner,et al. Using Functional or Structural Magnetic Resonance Images and Personal Characteristic Data to Identify ADHD and Autism , 2016, PloS one.
[62] Hyun-Jeong Lee,et al. Clinical Use of Continuous Performance Tests to Diagnose Children With ADHD , 2019, Journal of attention disorders.
[63] Daniel L. Rubin,et al. Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease , 2008, PLoS Comput. Biol..
[64] O. Chapelle,et al. Semi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) [Book reviews] , 2009, IEEE Transactions on Neural Networks.
[65] Boreom Lee,et al. Classification of ADHD subgroup with recursive feature elimination for structural brain MRI , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[66] B. Horta,et al. The worldwide prevalence of ADHD: a systematic review and metaregression analysis. , 2007, The American journal of psychiatry.
[67] Jürgen Margraf,et al. Is ADHD diagnosed in accord with diagnostic criteria? Overdiagnosis and influence of client gender on diagnosis. , 2012, Journal of consulting and clinical psychology.
[68] Nicolas P. Rougier,et al. Re-run, Repeat, Reproduce, Reuse, Replicate: Transforming Code into Scientific Contributions , 2017, Front. Neuroinform..
[69] M. W Gardner,et al. Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences , 1998 .
[70] Swathi P. Iyer,et al. Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRI data , 2012, Front. Syst. Neurosci..
[71] Fahad Saeed,et al. Towards quantifying psychiatric diagnosis using machine learning algorithms and big fMRI data , 2018, Big Data Analytics.
[72] K Inoue,et al. Clinical Evaluation of Attention-Deficit Hyperactivity Disorder by Objective Quantitative Measures , 1998, Child psychiatry and human development.
[73] Tongsheng Zhang,et al. Extreme Learning Machine-Based Classification of ADHD Using Brain Structural MRI Data , 2013, PloS one.
[74] Russell Greiner,et al. ADHD-200 Global Competition: diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements , 2012, Front. Syst. Neurosci..
[75] Ahmed El Gazzar,et al. Simple 1-D Convolutional Networks for Resting-State fMRI Based Classification in Autism , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).
[76] D. Linden. The Challenges and Promise of Neuroimaging in Psychiatry , 2012, Neuron.
[77] Xiaojin Zhu,et al. Semi-Supervised Learning , 2010, Encyclopedia of Machine Learning.
[78] W. Gouvier,et al. “Why Is This So Hard?” A Review of Detection of Malingered ADHD in College Students , 2014, Journal of attention disorders.
[79] Alan C. Evans,et al. Exploring Individual Brain Variability during Development based on Patterns of Maturational Coupling of Cortical Thickness: A Longitudinal MRI Study , 2019, Cerebral cortex.
[80] M. Mostafizur Rahman,et al. Addressing the Class Imbalance Problem in Medical Datasets , 2013 .
[81] Russell Greiner,et al. Learning to Classify Psychiatric Disorders based on fMR Images : Autism vs Healthy and ADHD vs Healthy , 2013 .
[82] Rich Caruana,et al. An empirical comparison of supervised learning algorithms , 2006, ICML.
[83] Lizhen Shao,et al. Deep Forest in ADHD Data Classification , 2019, IEEE Access.
[84] A. Mechelli,et al. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications , 2017, Neuroscience & Biobehavioral Reviews.
[85] Janaina Mourão Miranda,et al. Investigating the predictive value of whole-brain structural MR scans in autism: A pattern classification approach , 2010, NeuroImage.
[86] Ghassan Hamarneh,et al. Connectome priors in deep neural networks to predict autism , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[87] Thomas G. Dietterich. What is machine learning? , 2020, Archives of Disease in Childhood.
[88] Vince D. Calhoun,et al. Enhanced dynamic functional connectivity (whole-brain chronnectome) in chess experts , 2020, Scientific Reports.
[89] Ralph-Axel Müller,et al. Transient states of network connectivity are atypical in autism: A dynamic functional connectivity study , 2019, Human brain mapping.
[90] Peter Fransson,et al. Dynamic synergetic configurations of resting-state networks in ADHD , 2019, NeuroImage.
[91] R. Cameron Craddock,et al. Clinical applications of the functional connectome , 2013, NeuroImage.
[92] Fahad Saeed,et al. Auto-ASD-Network: A Technique Based on Deep Learning and Support Vector Machines for Diagnosing Autism Spectrum Disorder using fMRI Data , 2019, BCB.
[93] S. Rose,et al. A systematic review of structural MRI biomarkers in autism spectrum disorder: A machine learning perspective , 2018, International Journal of Developmental Neuroscience.
[94] Yi Pan,et al. Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier , 2019, Neurocomputing.
[95] Bruce Fischl,et al. FreeSurfer , 2012, NeuroImage.
[96] Liang Zou,et al. 3D Dense Separated Convolution Module for Volumetric Image Analysis , 2019, ArXiv.
[97] Chien-Chang Ho,et al. ADHD classification by a texture analysis of anatomical brain MRI data , 2012, Front. Syst. Neurosci..
[98] Alvis Cheuk M. Fong,et al. ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data , 2019, Front. Neuroinform..
[99] Enrico Pellegrini,et al. Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review , 2018, Alzheimer's & dementia.
[100] Naixue Xiong,et al. Spatio-temporal deep learning method for ADHD fMRI classification , 2019, Inf. Sci..
[101] Abbas Babajani-Feremi,et al. Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory , 2015, Clinical Neurophysiology.
[102] L. Paquet. [Economic impact]. , 2004, SADJ : journal of the South African Dental Association = tydskrif van die Suid-Afrikaanse Tandheelkundige Vereniging.
[103] Canhua Wang,et al. Functional connectivity-based classification of autism and control using SVM-RFECV on rs-fMRI data. , 2019, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.
[104] Nicha C. Dvornek,et al. Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks , 2017, MLMI@MICCAI.
[105] Juntang Zhuang,et al. Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI , 2019, MLMI@MICCAI.
[106] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[107] J. Rao,et al. The estimation of the mean squared error of small-area estimators , 1990 .
[108] Amir Mosavi,et al. Deep Learning: A Review , 2018 .
[109] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[110] Alex Martin,et al. Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards , 2014, NeuroImage: Clinical.
[111] Ayman El-Baz,et al. Identifying Personalized Autism Related Impairments Using Resting Functional MRI and ADOS Reports , 2018, MICCAI.
[112] Ayman El-Baz,et al. A novel CAD system for autism diagnosis using structural and functional MRI , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).
[113] F. Castellanos,et al. Intrinsic Functional Connectivity in Attention-Deficit/Hyperactivity Disorder: A Science in Development. , 2016, Biological psychiatry. Cognitive neuroscience and neuroimaging.
[114] Sahar Kianian,et al. Diagnosis of attention deficit hyperactivity disorder using deep belief network based on greedy approach , 2017, 2017 5th International Symposium on Computational and Business Intelligence (ISCBI).
[115] Annie A. Garner,et al. Parent-teacher agreement on ADHD symptoms across development. , 2015, Psychological assessment.
[116] David Coghill,et al. Brainstem abnormalities in attention deficit hyperactivity disorder support high accuracy individual diagnostic classification , 2014, Human brain mapping.
[117] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[119] D. Willshaw,et al. Cerebral Cortex doi:10.1093/cercor/bhr221 Cerebral Cortex Advance Access published September 21, 2011 Similarity-Based Extraction of Individual Networks from Gray Matter MRI Scans , 2022 .
[120] Monika D. Heller,et al. The Groundskeeper Gaming Platform as a Diagnostic Tool for Attention-Deficit/Hyperactivity Disorder: Sensitivity, Specificity, and Relation to Other Measures. , 2016, Journal of child and adolescent psychopharmacology.
[121] Joseph S. Raiker,et al. Accuracy of Achenbach Scales in the Screening of Attention-Deficit/Hyperactivity Disorder in a Community Mental Health Clinic. , 2017, Journal of the American Academy of Child and Adolescent Psychiatry.
[122] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[123] Sergio Escalera,et al. Automatic brain caudate nuclei segmentation and classification in diagnostic of Attention-Deficit/Hyperactivity Disorder , 2012, Comput. Medical Imaging Graph..
[124] Andrew Simmons,et al. Pattern classification of response inhibition in ADHD: Toward the development of neurobiological markers for ADHD , 2013, Human brain mapping.
[125] W. Pelham,et al. The economic impact of attention-deficit/hyperactivity disorder in children and adolescents. , 2007, Journal of pediatric psychology.
[126] Eduardo Alonso,et al. Phenotypic Integrated Framework for Classification of ADHD Using fMRI , 2016, ICIAR.
[127] Hailong Li,et al. Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method , 2017, Front. Neurosci..
[128] Dinggang Shen,et al. Early Diagnosis of Autism Disease by Multi-channel CNNs , 2018, MLMI@MICCAI.
[129] Aleksander Madry,et al. How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift) , 2018, NeurIPS.
[130] Stephen J. Blumberg,et al. Prevalence of Parent-Reported ADHD Diagnosis and Associated Treatment Among U.S. Children and Adolescents, 2016 , 2018, Journal of clinical child and adolescent psychology : the official journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53.
[131] Michel F. Valstar,et al. Automatic Detection of ADHD and ASD from Expressive Behaviour in RGBD Data , 2016, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).
[132] Russell Greiner,et al. A general prediction model for the detection of ADHD and Autism using structural and functional MRI , 2018, PloS one.
[133] A. Hao,et al. Discrimination of ADHD children based on Deep Bayesian Network , 2015 .
[134] Bo Zhang,et al. Classification based on neuroimaging data by tensor boosting , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).
[135] Peter B. Jones,et al. Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation , 2017, Neuron.
[136] Nan Jia,et al. SAE-based classification of school-aged children with autism spectrum disorders using functional magnetic resonance imaging , 2018, Multimedia Tools and Applications.
[137] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[138] Shao-Wei Xue,et al. Linking graph features of anatomical architecture to regional brain activity: A multi-modal MRI study , 2017, Neuroscience Letters.
[139] Aixia Zhang,et al. Altered dynamic functional connectivity across mood states in bipolar disorder , 2020, Brain Research.
[140] Vigneshwaran Subbaraju,et al. Accurate detection of autism spectrum disorder from structural MRI using extended metacognitive radial basis function network , 2015, Expert Syst. Appl..
[141] Ralph-Axel Müller,et al. Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism , 2015, NeuroImage: Clinical.
[142] Erik Linstead,et al. Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review , 2019, Review Journal of Autism and Developmental Disorders.
[143] Bogdan Wilamowski,et al. Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Classification From Functional Magnetic Resonance Imaging Data , 2015, IEEE Transactions on Cybernetics.
[144] Robert Hecht-Nielsen,et al. Theory of the backpropagation neural network , 1989, International 1989 Joint Conference on Neural Networks.
[145] Ljupco Kocarev,et al. Machine learning approach for classification of ADHD adults. , 2014, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.
[146] Ben Glocker,et al. Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease , 2018, Medical Image Anal..
[147] Ning Li,et al. Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data , 2020, Complex..
[148] J. Buitelaar,et al. A graph theory study of resting-state functional connectivity in children with Tourette syndrome , 2020, Cortex.
[149] Joshua M. Langberg,et al. Variability in ADHD Care in Community-Based Pediatrics , 2014, Pediatrics.
[150] J. Stockman,et al. Prevalence of Parent-Reported Diagnosis of Autism Spectrum Disorder Among Children in the US, 2007 , 2011 .
[151] Shana Nichols,et al. A Review of the Validity of Laboratory Cognitive Tasks Used to Assess Symptoms of ADHD , 2003, Child psychiatry and human development.
[152] Vince D. Calhoun,et al. Dynamic functional connectivity in schizophrenia and autism spectrum disorder: Convergence, divergence and classification , 2019, NeuroImage: Clinical.
[153] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[154] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[155] M. Bellgrove,et al. Altered structural connectivity in ADHD: a network based analysis , 2016, Brain Imaging and Behavior.
[156] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[157] Qianzi Shen,et al. Dilated 3D Convolutional Neural Networks for Brain MRI Data Classification , 2019, IEEE Access.
[158] Qi Sun,et al. The Diagnosis of Autism Spectrum Disorder Based on the Random Neural Network Cluster , 2018, Front. Hum. Neurosci..
[159] J. Pillai. Functional Connectivity. , 2017, Neuroimaging clinics of North America.
[160] Weihao Zheng,et al. Multi-Feature Based Network Revealing the Structural Abnormalities in Autism Spectrum Disorder , 2021, IEEE Transactions on Affective Computing.
[161] A summary of psychiatry , 1967 .
[162] Arnaud Cachia,et al. Feature selection and classification of imbalanced datasets Application to PET images of children with autistic spectrum disorders , 2011, NeuroImage.
[163] Dinggang Shen,et al. Identifying Autism Spectrum Disorder With Multi-Site fMRI via Low-Rank Domain Adaptation , 2020, IEEE Transactions on Medical Imaging.
[164] X. Ke,et al. Diagnostic model generated by MRI‐derived brain features in toddlers with autism spectrum disorder , 2017, Autism research : official journal of the International Society for Autism Research.
[165] N. Logothetis,et al. Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.
[166] Mark S. Cohen,et al. Insights into multimodal imaging classification of ADHD , 2012, Front. Syst. Neurosci..
[167] Ayman El-Baz,et al. Using resting state functional MRI to build a personalized autism diagnosis system , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[168] Huafu Chen,et al. Abnormal dynamic functional connectivity density in patients with generalized anxiety disorder. , 2020, Journal of affective disorders.
[169] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[170] Elizabeth B. Owens,et al. Defining ADHD symptom persistence in adulthood: optimizing sensitivity and specificity , 2017, Journal of child psychology and psychiatry, and allied disciplines.
[171] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[172] Camel Tanougast,et al. Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder , 2017, BMC Neuroscience.
[173] A. Fagan,et al. Functional connectivity and graph theory in preclinical Alzheimer's disease , 2014, Neurobiology of Aging.
[174] Hongen Liao,et al. Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects , 2019, IEEE Reviews in Biomedical Engineering.
[175] Lianghua He,et al. Classification on ADHD with Deep Learning , 2014, 2014 International Conference on Cloud Computing and Big Data.
[176] Hang Joon Jo,et al. Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study , 2016, PloS one.
[177] Philippe Fortemps,et al. A multi-level classification framework for multi-site medical data: Application to the ADHD-200 collection , 2018, Expert Syst. Appl..
[178] Tetsuya Iidaka,et al. Resting state functional magnetic resonance imaging and neural network classified autism and control , 2015, Cortex.
[179] Yun Jiao,et al. Diagnostic model for attention-deficit hyperactivity disorder based on interregional morphological connectivity , 2018, Neuroscience Letters.
[180] Daniel Brandeis,et al. Classifying adolescent attention-deficit/hyperactivity disorder (ADHD) based on functional and structural imaging , 2015, European Child & Adolescent Psychiatry.
[181] Yu-Feng Zang,et al. Inconsistency in Abnormal Brain Activity across Cohorts of ADHD-200 in Children with Attention Deficit Hyperactivity Disorder , 2017, Front. Neurosci..
[182] Fahad Saeed,et al. GPU-PCC: A GPU Based Technique to Compute Pairwise Pearson's Correlation Coefficients for Big fMRI Data , 2017, BCB.
[183] Dimitri Van De Ville,et al. The dynamic functional connectome: State-of-the-art and perspectives , 2017, NeuroImage.
[184] Arvind Kumar Tiwari,et al. A Survey of Machine Learning Based Approaches for Parkinson Disease Prediction , 2015 .
[185] Stephen T. C. Wong,et al. Transductive Maximum Margin Classification of ADHD Using Resting State fMRI , 2016, MLMI@MICCAI.
[186] Lili He,et al. Enhancing Diagnosis of Autism With Optimized Machine Learning Models and Personal Characteristic Data , 2019, Front. Comput. Neurosci..
[187] Philippe Fortemps,et al. Towards interpretable machine learning models for diagnosis aid: A case study on attention deficit/hyperactivity disorder , 2019, PloS one.
[188] Antoine Grigis,et al. Quantifying performance of machine learning methods for neuroimaging data , 2019, NeuroImage.