A general prediction model for the detection of ADHD and Autism using structural and functional MRI
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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 reported modest, but not clinically useful, accuracy of a complex machine learning algorithm attempting to classify individuals with ASD or ADHD versus TD individuals.
“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
Biological links such as single nucleotide polymorphisms, brain structure, or functional connectivity to specific disorders are limited.
“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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- 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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