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A general prediction model for the detection of ADHD and Autism using structural and functional MRI
Open accessAbstract
This work presents a novel method for learning a model that can diagnose Attention Deficit Hyperactivity Disorder (ADHD), as well as Autism, using structural texture and functional connectivity features obtained from 3-dimensional structural magnetic resonance imaging (MRI) and 4-dimensional resting-state functional magnetic resonance imaging (fMRI) scans of subjects. We explore a series of three learners: (1) The LeFMS learner first extracts features from the structural MRI images using the texture-based filters produced by a sparse autoencoder. These filters are then convolved with the original MRI image using an unsupervised convolutional network. The resulting features are used as input to a linear support vector machine (SVM) classifier. (2) The LeFMF learner produces a diagnostic model by first computing spatial non-stationary independent components of the fMRI scans, which it uses to decompose each subject's fMRI scan into the time courses of these common spatial components. These features can then be used with a learner by themselves or in combination with other features to produce the model. Regardless of which approach is used, the final set of features are input to a linear support vector machine (SVM) classifier. (3) Finally, the overall LeFMSF learner uses the combined features obtained from the two feature extraction processes in (1) and (2) above as input to an SVM classifier, achieving an accuracy of 0.673 on the ADHD-200 holdout data and 0.643 on the ABIDE holdout data. Both of these results, obtained with the same LeFMSF framework, are the best known, over all hold-out accuracies on these datasets when only using imaging data-exceeding previously-published results by 0.012 for ADHD and 0.042 for Autism. Our results show that combining multi-modal features can yield good classification accuracy for diagnosis of ADHD and Autism, which is an important step towards computer-aided diagnosis of these psychiatric diseases and perhaps others as well.
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“A recent study that took advantage of both structural and functional neuroimaging data reported modest, but not clinically useful, accuracy of a complex machine learning algorithm attempting to classify individuals with ASD or ADHD versus TD individuals (Sen et al., 2018).”
From Investigating functional brain network integrity using a traditional and novel categorical scheme for neurodevelopmental disorders (2019) · cites this paper as Sen et al., 2018
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“Biological links such as single nucleotide polymorphisms (4,5), brain structure (6,7), or functional connectivity (8,9) to specific disorders are limited.”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [9]
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Items 101–199 of 199
ASDFace: Face-based Autism Diagnosis via Heterogeneous Domain Adaptation
Wang, H., Chi, L., Su, C., & Zhao, Z. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557170 · Cited by 9
“Neuroimaging data has also been studied using advanced machine learning models, such as MRI and Functional MRI (fMRI) [27, 28].”
MethodPredicting adult Attention Deficit Hyperactivity Disorder (ADHD) using vocal acoustic featuresPreprint
von Polier, G. G., Ahlers, E., Amunts, J., Langner, J., Patil, K. R., Eickhoff, S. B., Helmhold, F., & Langner, D. (2021) · medRxiv · doi:10.1101/2021.03.18.21253108 · Cited by 10
“Looking at the studies that have investigated the machine learning based prediction of ADHD from other biological signals points towards a similar performance of approaches based on neuropsychological performance [65], EEG-measures [55, 66], questionnaires [67] or resting state fMRI [68-70] as compared to our findings.”
ResultApplication of TBSS-based machine learning models in the diagnosis of pediatric autism
He, X., Zhao, X., Sun, Y., Geng, P., & Zhang, X. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2022.1078147 · Cited by 8
“However, some researches have reported appropriate systematic rehabilitation interventions in the early stages of autism could improve the symptoms of most patients (7, 8).”
BackgroundPredicting childhood ADHD-linked symptoms from prenatal and perinatal data in the ABCD cohort.
Dooley, N., Healy, C., Cotter, D., Clarke, M., & Cannon, M. (2023) · Development and Psychopathology · doi:10.1017/s0954579423000238 · Cited by 5
“MRIs remain expensive and non-routine in child psychiatry assessment, and the current MRI-based classifiers are only capable of concurrently identifying ADHD children that have already been clinical diagnosed (e.g., Sen et al., 2018).”
BackgroundFusion of Features: A Technique to Improve Autism Spectrum Disorder Detection Using Brain MRI Images
Dhamale, T. D., Bhandari, S. U., & Harpale, V. K. (2023) · Biomedical & Pharmacology Journal · doi:10.13005/bpj/2819 · Cited by 9
Research Advances in ADHD and Technology
Cibrian, F. L., Hayes, G. R., & Lakes, K. D. (2020) · Synthesis lectures on assistive, rehabilitative, and health-preserving technologies · doi:10.2200/s01061ed1v01y202011arh015 · Cited by 8
Narayanan, N., Remya, K. R., & Varghese, B. M. (2024) · International Journal of Hybrid Intelligent Systems · doi:10.3233/his-240029 · Cited by 7
Sallibi, A. D., & Alheeti, K. M. A. (2023) · International Conference on Decision Aid Sciences and Applications (DASA) · doi:10.1109/dasa59624.2023.10286808 · Cited by 7
“This study [19] suggests an artificial intelligence-based device that tracks the patient's condition using sensing data and changes the learning process by giving the patient engaging activities and assignments based on their mood and facial expression.”
BackgroundÖztekin, I., Garic, D., Bayat, M., Hernandez, M. L., Finlayson, M. A., Graziano, P. A., & Dick, A. S. (2022) · European Journal of Neuroscience · doi:10.1111/ejn.15842 · Cited by 8
“…focusing on cortical thickness measures (Colby et al., 2012; Ghiassian et al., 2016; Öztekin et al., 2021; Peng et al., 2013; Qureshi et al., 2017; Sen et al., 2018), with the majority of studies focusing on functional MRI, resting-state data (Colby et al., 2012; Dai et al., 2012; Du et al.,…”
BackgroundProvenzano, D., Washington, S. D., Rao, Y. J., Loew, M. H., & Baraniuk, J. N. (2020) · Brain Sciences · doi:10.3390/brainsci10050319 · Cited by 7
“[17] Features were extracted from pre-processed data, iteratively passed through a feature reduction method, trained on a set of learning data, tested on a validation set, and cross validated to evaluate performance.”
MethodChen, J., Zhang, H., Zou, Q., Liao, B., & Bi, X.-A. (2024) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-024-00629-8 · Cited by 6
Rahman, M. M. (2025) · Frontiers in Child and Adolescent Psychiatry · doi:10.3389/frcha.2025.1504323 · Cited by 8
“Furthermore, our classi fi er outperformed another study conducted on a separate sample of 240 children, which utilized the temporal variability of dynamic functional connectivity from MRI brain images, achieving an accuracy of 0.78 and an AUC score of 0.84 (44) based on diagnostic model SVM.”
InfluentialMasood, K., & Kashef, R. F. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-09342-5_11 · Cited by 6
Concatenating framework in ASD analysis towards research progress
Roopa, B. S., & Prasad, R. M. (2019) · International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE) · doi:10.1109/icatiece45860.2019.9063816 · Cited by 6
“Further in[14] &[15] convolution neural network is proposed which handles multi modality like Functional MRI and Structural MRI by extracting the 512 relevant features to learn through back propagation from both imaging modalities and architectures a convolution network layering respectively.”
MethodAutism screening for children based on appearance features across multiple paradigms
Lu, X., Yue, S., Li, C., Yang, X., Wang, Y., & Liu, Z. (2025) · Displays · doi:10.1016/j.displa.2025.103049 · Cited by 5
Artificial Intelligence for Autism Spectrum Disorders
Ferrari, E. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_249-1 · Cited by 5
Shakur, A. H., Sun, T., Kim, J.-E., & Huang, S. (2022) · IISE Transactions on Healthcare Systems Engineering · doi:10.1080/24725579.2022.2126036 · Cited by 5
“For example, brain imaging methods have been explored as diagnostic aids (Sato et al., 2012; Sen et al., 2018; Bohland et al., 2012) based on MRI images and resting-state fMRI scans.”
BackgroundMethodBandyopadhyay, S., Sarma, M., & Samanta, D. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2025.110117 · Cited by 5
Early autism diagnosis based on path signature and Siamese unsupervised feature compressor
Yin, Z., Ding, X., Zhang, X., Wu, Z., Wang, L., Xu, X., & Li, G. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae069 · Cited by 8
Fuzzy Based Integrated Diagnostic System for Neurodevelopmental Disorders
Kaur, G., & Kakkar, D. (2019) · International Conference on Signal Processing and Integrated Networks (SPIN) · doi:10.1109/spin.2019.8711674 · Cited by 7
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The Expanding Frontier: The Role of Artificial Intelligence in Pediatric Neuroradiology
Guarnera, A., Napolitano, A., Liporace, F., Marconi, F., Rossi‐Espagnet, M. C., Gandolfo, C., Romano, A., Bozzao, A., & Longo, D. (2025) · Children · doi:10.3390/children12091127 · Cited by 4
Attention Deficit Hyperactivity Disorder(ADHD) Prediction System
Dubey, A. K., Saxena, A., Kumar, A., & Rai, A. (2022) · International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · doi:10.1109/icscds53736.2022.9760921 · Cited by 5
USMDA: Unsupervised Multisource Domain Adaptive ADHD prediction model using neuroimaging
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Prediction of Learning Disorder: A-Systematic Review
Jamhar, M. A., Salwana, E., Zulkifli, Z., Nayan, N. M., & Abdullah, N. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34032-2_38 · Cited by 4
Neurobiological Clusters Are Associated With Trajectories of Overall Psychopathology in Youth
Wang, C. K., Hayes, R. A., Roeder, K., & Jalbrzikowski, M. (2023) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2023.04.007 · Cited by 6
Diagnosis of Attention Deficit Hyperactivity Disorder
Salgotra, K., Khullar, V., Singh, H. P., & Khan, S. A. (2021) · Advances in web technologies and engineering book series · doi:10.4018/978-1-7998-7511-6.ch003 · Cited by 5
Gholami, S., Motamed, S., & Askari, E. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.114655 · Cited by 3
MAN-GNN: An interpretable biomarker architecture for neurodevelopmental disorders
Han, Q., Ye, H., Bai, M., Wang, L., Sun, Y., Song, Z., Zhao, J. M., Shi, L., & Kuang, Z. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108110 · Cited by 3
Elnakieb, Y. A., Ali, M. T., Soliman, A., Mahmoud, A. H., Shalaby, A. M., Switala, A. E., Ghazal, M. A., Khalil, A., Fraiwan, L., Barnes, G. N., et al. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822822-7.00018-1 · Cited by 3
Artificial Intelligence for Autism Spectrum Disorders
Ferrari, E. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_249 · Cited by 3
An assessment of the autism neuroimaging literature for the prospects of re-executability
Hodge, S. M., Haselgrove, C., Honor, L. B., Kennedy, D. N., & Frazier, J. A. (2020) · F1000Research · doi:10.12688/f1000research.25306.1 · Cited by 3
Application of machine learning in early identification and diagnosis of autistic children
Tingting, H. O. U., Xiao, C. H. E. N., Kong, D., Xiujun, S. H. A. O., Lin, F., & Kaiyun, L. I. (2022) · Advances in Psychological Science · doi:10.3724/sp.j.1042.2022.02321 · Cited by 3
“…AQ-10, Q-CHAT-10 CNN, C4.5, BN, Ridor, DL 97.95%/98.63%/96.53%/- 49 伊朗 Salari et al.(2019) 505ASD,530TC av10～33.7 岁 87.7;82.1 fMRI CNN, SVM, KNN, RF 70.22%/77.46%/61.82%/74.86% 50 加拿大 Sen et al.(2018) 1783 av0～30 岁 72.6 sMRI, fMRI SVM, PCA Acc-=67.3%; 64.2% 51 加拿大 Sewani et al.(2020) 539ASD,573TC…”
InfluentialA Study of the ADHD-200 Repository for Attention Deficit Categorization Using Deep Neural Networks
Mengi, M., & Malhotra, D. (2023) · International Conference on Advances in Electronics Computers and Communications · doi:10.1109/icaecc59324.2023.10560312 · Cited by 3
“More information about the repository can be accessed by the following link: http://fcon1000.projects.nitrc.org/indi/adhd-200/ [ 5 ] Data on training and testing, as well as the phenotypic information, is presented in Table 1.”
Khandelwal, D. K., & Govil, M. C. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-21029-0 · Cited by 3
“Inappropriate hyperactivity is frequently mistaken for the neuropsychiatric disorder’s symptoms [ 4 ].”
Predicting Depression Levels using Back Propagation Neural Network
Dharma, E. M., Heryadi, Y., Lukas, L., Suparta, W., & Wibowo, A. (2022) · Journal of Computer Science · doi:10.3844/jcssp.2022.151.161 · Cited by 3
“Furthermore, Sen et al. (2018), used s-MRI and rs-fMRI as predictors and produced an accuracy of 64.3%.”
MethodInfluentialTowards Explainable Multi-Modal Fusion Strategies for ASD Detection: A Review
El-Askary, N. S., Gawish, M., Morsey, M. M., Mahmoud, A. M., Aref, M. M., & Elarif, T. (2025) · International Conference on Intelligent Computing and Information Systems (ICICIS) · doi:10.1109/icicis66182.2025.11313196 · Cited by 2
Bandyopadhyay, S., Sarma, M., & Samanta, D. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10723846 · Cited by 2
“Sen et al. [14] utilized ICA for ROI extraction and classified ADHD with 67.3% accuracy, while Mao et al. employed LSTM and spatiotemporal convolution to classify ADHD with a 71.3% accuracy score.”
MethodInfluentialSütçübaşı, B., Ballı, T., Roeyers, H., Wiersema, J. R., Çamkerten, S., Öztürk, O. C., Metin, B., & Sonuga-Barke, E. J. S. (2025) · Journal of Attention Disorders · doi:10.1177/10870547251315230 · Cited by 2
“Our approach using connectivity measures as features yielded higher accuracy values compared to the previous studies employing different datasets (see Ghiassian et al., 2016; Sen et al., 2018).”
Hosseini, E., Hosseini, S. A., Servaes, S., Hall, B., Rosa‐Neto, P., Moradi, A., Kumar, A., Pedram, M. M., & Chawla, S. (2025) · Tomography · doi:10.3390/tomography11050056 · Cited by 2
“Using ADHD-200 holdout data in a study, Sen et al. [12] achieved an accuracy of 0.67.”
InfluentialOn Prediction Models for the Detection of Autism Spectrum Disorder
Biswas, S. D., Chakraborty, R., & Pramanik, A. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-2449-3_31 · Cited by 2
Normal development of the brain: a survey of joint structural–functional brain studies
Namiranian, R., Malakshan, S. R., Moghaddam, H. A., Khadem, A. A., & Jafari, R. (2022) · Reviews in the Neurosciences · doi:10.1515/revneuro-2022-0017 · Cited by 4
The use of artificial intelligence to predict depression through thermal imaging
Dharma, E. M., Prabowo, H., Trisetyarso, A., & Wiguna, T. (2023) · AIP conference proceedings · doi:10.1063/5.0163192 · Cited by 2
HIROSE, S. (2024) · Japanese Journal of Physiological Psychology and Psychophysiology · doi:10.5674/jjppp.2409si · Cited by 2
Automated diagnosis of autism: State of the artPreprint
Valizadeh, A., Moassefi, M., Nakhostin-Ansari, A., Some’eh, S. H., Hosseini-Asl, S. H., Torbati, M. S., Aghajani, R., Ghorbani, Z. M., Oskouie, I. M., Aghajani, F., et al. (2021) · medRxiv · doi:10.1101/2021.06.29.21254249 · Cited by 2
Finding essential parts of the brain in rs-fMRI can improve diagnosing ADHD by Deep LearningPreprint
Kim, B., Park, J., Kim, T., & Kwon, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.10137 · Cited by 2
“(Greicius [2008], Zhu et al. [2008], Dey et al. [2014], Guo et al. [2014], Lin et al. [2014], Sen et al. [2018], Tang et al. [2021]) They tried to classify ADHD using FC.”
MethodEnhancing ADHD Prediction in Adolescents through Fitbit-Derived Wearable DataPreprint
Rahman, M. M. (2024) · medRxiv · doi:10.1101/2024.09.20.24314084 · Cited by 2
Mishra, M., & Pati, U. C. (2025) · Intelligent Data Analysis · doi:10.1177/1088467x251330464 · Cited by 1
Liu, X., Yang, Z., Lu, Y., Tan, S., Wang, K., Wang, C., Wang, N., Feng, Z., Wei, H., Cheng, M., et al. (2025) · European journal of medical research · doi:10.1186/s40001-025-02815-w · Cited by 1
Baboli, R., Martin, E., Qiu, Q., Zhao, L., Liu, T.-M., Wu, K., Gao, Y., & Li, X.-B. (2026) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2026.111966
“…approaches, SVM classifiers successfully distinguished children with ADHD from typically developing peers on the basis of structural MRI, resting-state fMRI, or diffusion tensor imaging data(Fair et al., 2013, Yasumura et al., 2020, Sen et al., 2018, Johnston et al., 2014, Cheng et al., 2012).”
Mishra, M., & Pati, U. C. (2026) · SN Computer Science · doi:10.1007/s42979-025-04609-7 · Cited by 1
“In [25 ], functional connectivity along with structural texture using fMRI and sMRI has been utilized in detect - ing ADHD and Autism with 573 Controls and 538 ASD subjects.”
Bousmaha, R., Meribai, K., Bouchemal, N., Bouchemal, N., & Ivanova, G. (2026) · Bioengineering · doi:10.3390/bioengineering13050586 · Cited by 1
“In 2018, Sen et al. [15] proposed a diagnostic method utilizing structural texture and functional connectivity features extracted from MRI scans in the ADHD-200 dataset, achieving notable accuracy through linear support vector machine (SVM) classifiers.”
Golagani, E. H., Masoudnia, S., Kalhor, A., & Hamid Soltanian-zadeh, A. (2026) · Scientific Reports · doi:10.1038/s41598-026-73268-2 · Cited by 1
Johnson, L. E., & Conrad, J. M. (2018) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon.2018.8796555 · Cited by 1
“indications were discovered by utilizing machine learning techniques to create better classifiers which found that ADHD participants have reduced gray matter volume primarily in the frontal lobe, and differing visio-spatial and DMN connectivity versus their neurotypical counterparts [32]–[34].”
BackgroundMeira, M. D. O., Canuto, A. M. P., de Carvalho, B. M., & Jales, R. L. C. (2022) · Research Society and Development · doi:10.33448/rsd-v11i8.31258 · Cited by 3
Mezrioui, N. E. H., Aloui, K., Naït-Ali, A., & Naceur, M. S. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140775 · Cited by 2
“Bhashkar Sen, et al[18] ADHD: 491 HC et 279 ASD ABIDE: 573 HC et 538 ASD ADHD-200 (including 8 sites)”
BackgroundExploring the Potential of Machine Learning in Healthcare Accuracy Improvement
Veeramani, S., Ramesh, S. M., & Gomathy, B. (2023) · WSEAS Transactions on Computers archive · doi:10.37394/23205.2023.22.42 · Cited by 1
A Nomogram for Predicting ADHD and ASD in Child and Adolescent Mental Health Services (CAMHS)
Blasco-Fontecilla, H., Li, C., Vizcaino, M. C. R., Fernández-Fernández, R., Royuela, A., & Bella-Fernández, M. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13082397 · Cited by 1
“Regarding ASD, some predictive models have been proposed based on neurobiological markers [14,19,20] or screening tests [21].”
MethodfMRI-Based Multi-class DMDC Model Efficiently Decodes the Overlaps between ASD and ADHD
Zolghadr, Z., Batouli, S. A. H., Majd, H. A., Shafaghi, L., & Mehrabi, Y. (2024) · Basic and Clinical Neuroscience Journal · doi:10.32598/bcn.2023.4302.1 · Cited by 2
“One recent study, Sen et al. (2018) aimed to identify and classify neuroimaging data using structural and functional connectivity characteristics, exploring a series of three learners.”
BackgroundMethodElashram, R. E. M., Aldaghmi, O. A., & Mohammed, S. M. (2024) · Journal of Disability Research · doi:10.57197/jdr-2024-0083 · Cited by 1
Deepika, D., Sharma, M., & Arora, S. (2024) · Journal of medical imaging · doi:10.1117/1.jmi.11.6.064502 · Cited by 2
Li, X., Huang, W., Tan, R., Xu, C., Chen, X., Zhang, Q., Li, S., Liu, Y., Qiu, H., Bi, C., et al. (2025) · Current Neuropharmacology · doi:10.2174/011570159x332833241222191422 · Cited by 2
“A structural and functional MRI prediction model for ADHD and autism has been developed [90] and the use of machine learning can applied to for behavioral distinction of autism and ADHD [42].”
An assessment of the autism neuroimaging literature for the prospects of re-executability
Hodge, S. M., Haselgrove, C., Honor, L. B., Kennedy, D. N., & Frazier, J. A. (2021) · F1000Research · doi:10.12688/f1000research.25306.2 · Cited by 1
Haddadpour, M., Wallois, F., & Saadatmand‐Tarzjan, M. (2020) · Iranian Joint Congress on Fuzzy and Intelligent Systems · doi:10.1109/cfis49607.2020.9238675 · Cited by 1
“[25] proposed a novel method for learning a model that can diagnosis ADHD and Autism using structural texture and functional connectivity features obtained from 3-D MRI and 4-D resting state f MRI(rs-f MRI) of each subject.”
MethodRai, N., Pradhan, P. C., Saikia, H., Singh, O. P., & Bhutia, R. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-5090-2_12 · Cited by 1
Static and Dynamic Connectivity Analysis of Human Brain via Functional Magnetic Resonance ImagingPreprint
Sen, B. (2022) · Thesis Commons (OSF Preprints) · doi:10.31237/osf.io/3kunr · Cited by 1
Self-citationAn Autoencoder-Based Deep Learning Classifier for Efficient Diagnosis of Autism
Sewani, H. (2023) · doi:10.32920/24050754 · Cited by 1
Bhavna, K., Ghosh, N., Banerjee, R., & Roy, D. (2024) · medRxiv · doi:10.1101/2024.07.17.24310610 · Cited by 1
“Many similar studies using feature selection based on graph data has been carried out for other mental disorders such as Schizophrenia and Attention Deficit Hyperactivity Disorder(ADHD) [48, 49, 50].”
BackgroundEarly Detection of Autism Spectrum Disorder in Children Using Supervised Machine Learning
Vakadkar, K., Purkayastha, D., & Krishnan, D. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-6634-9_29
Retracted: Comparative study on predicting ADNP syndrome using deep learning algorithms
Kumar, V. U., Maruthi, P. D. S., Teja, P. V., & Sai, A. V. (2021) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec51865.2021.9591933
RetractedPatel, V. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.735861475.793563123
New Research in Children with Neurodevelopmental Disorders
Ayuso, D. M. R. (2022) · MDPI eBooks · doi:10.3390/books978-3-0365-4391-8
Meira, M. D. O., Canuto, A. M. P., de Carvalho, B. M., & Jales, R. L. C. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892968 · Cited by 1
“The study of ML techniques using medical images for the diagnosis of mental disorders is already well established in the literature, especially in Alzheimer's and Parkinson's disease [18-40], but also in ADHD [41-54].”
BackgroundRecent Trends in Automatic Autism Spectrum Disorder Detection Using Brain MRI
Dhamale, T. D., & Bhandari, S. U. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-7874-6_27
The role of MRI in diagnosing autism: a machine learning perspective.
Elnakieb, Y. A. (2022) · doi:10.18297/etd/4003
Li, X., Huang, W. W., Tan, R., Xu, C., Chen, X., Zhang, Q., Li, S., Liu, Y., Qiu, H., Cao, H., et al. (2023) · doi:10.2196/preprints.47213
Zhu, S., Sun, D., Liu, Z., Zhao, S., Yang, Y., & Chen, F. (2024) · Journal of Electronic Imaging · doi:10.1117/1.jei.33.2.023032
“Bhaskar et al. 16 advanced a method that leverages 3D structural magnetic resonance imaging and 4D resting-state fMRI for ADHD categorization.”
MethodKodukula, S. S., Varanasi, U. B., Boddikura, S., Talluri, P., & Kalepu, B. S. (2024) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-031-51167-7_76
predict bipolar disorder by using graph theory, cortical volume, and machine learningPreprint
Ruhi, M., & Hassani‐Abharian, P. (2024) · Research Square · doi:10.21203/rs.3.rs-5269996/v1
Classification of ASD Based on fMRI Data Using Two Hybrid Learning Approaches
Gerdefaramarzi, S. S., & Hakimdavoodi, H. (2024) · Iranian Conference on Intelligent Systems (ICIS) · doi:10.1109/icis64839.2024.10887421
“In another study, (Sen, Borle, Greiner, & Brown, 2018) implemented a model using structural texture and functional connectivity features extracted from 3-dimensional structural magnetic resonance imaging (MRI) and 4-dimensional resting-state fMRI scans of 1111 total healthy and ASD subjects and…”
Eskorouchi, A., Wang, H., Lee, J. W., Nayak, V. H., Ojeda, N. B., & Fan, L. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10981031
“Sen et al. (2018) developed a model for diagnosing ADHD and autism using MRI and resting-state fMRI data, achieving high classification accuracies [8].”
La República del Perú y el Estado de Israel. Análisis de la relación bilateral
Denegri, E. F. (2025) · Agenda Internacional · doi:10.18800/agenda.202501.005 · Cited by 1
Williams, D. R. P., Sudha, R., Justin, Z., Dhavamani, L., Khanna, H. N., & Chauhan, A. K. S. (2025) · International Conference on Data Science and Information System (ICDSIS) · doi:10.1109/icdsis65355.2025.11070324
“They compare and watch the accuracy of each classifier [19].”
Review of the existing research applying DL related to mental health conditions.
Kabongo, S. (2021) · Open Research Knowledge Graph (ORKG) · doi:10.48366/r139050
Li, H., Cui, L., Cao, L., Zhang, Y., Liu, Y., Deng, W., & Zhou, W. (2020) · Figshare · doi:10.6084/m9.figshare.13059188
Multi-Class Neuro Degenerative Disease Classifiers for Brain Mri Using Swin Transformer
R, B. R. M., Kavitha, K., & Jayasudha, J. S. (2025) · Innovations in Power and Advanced Computing Technologies (i-PACT) · doi:10.1109/i-pact65952.2025.11307982 · Cited by 1
An intelligent deep learning approach to recognize autism spectrum disorder
Kundu, R., & Yadav, S. K. (2026) · AIP conference proceedings · doi:10.1063/5.0298792
Machine learning approach to imaging-based classification of neurodevelopmental disorders
Martin, L. A., & Kana, R. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-26466-5.00015-5
A novel audio-visual model with real-time gradient modulation for mental disorder detection
Li, Y., Li, Y., Nair, R., & Naqvi, S. M. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110164
Hidayat, E. Y., Firdausillah, F., Dewi, I. N., Udayanti, E. D., Nugraha, A., Luthfiarta, A., Hidayat, N. N., Karima, A., & Muda, A. K. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115352
ADHD Diagnosis and Recognition Based on Functional Classification
Zheng, F., & Cao, C. (2020) · Journal of Information and Computing Science · doi:10.4208/jics.v15.n2.p141
Metadata
- Authors
- Sen, B., Borle, N. C., Greiner, R., & Brown, M. R. G.
- Affiliations
- University of Alberta (CA)
- Corresponding author
- Brown, Matthew R. G.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-04-17
- Volume
- 13
- Issue
- 4
- Pages
- e0194856
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC5903601
- OpenAlex
- W2801745895
- Cited by
- 198
- References
- 41
- Citations by year
- 2026: 9 · 2025: 20 · 2024: 28 · 2023: 25 · 2022: 31 · 2021: 31 · 2020: 36 · 2019: 13 · 2018: 5
- FWCI
- 12.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Canadian Institutes of Health Research (CSE125741)
- Natural Sciences and Engineering Research Council of Canada (NSERC) (203245)
- Alberta Machine Intelligence Institute (AMII)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Attention Deficit Hyperactivity Disorder
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Keywords
- ADHD diagnosis, autism diagnosis, structural MRI, resting-state fMRI, functional connectivity, texture features, sparse autoencoders, independent component analysis, support vector machine, computer-aided diagnosis, multimodal fusion, mental illness
- MeSH terms
- Adolescent, Adult, Attention Deficit Disorder with Hyperactivity, Autistic Disorder, Brain, Child, Decision Support Techniques, Diagnosis, Computer-Assisted, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Prognosis, Young Adult, Datasets as Topic
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