Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD
Open accessAbstract
In the multimodal neuroimaging framework, data on a single subject are collected from inherently different sources such as functional MRI, structural MRI, behavioral and/or phenotypic information. The information each source provides is not independent; a subset of features from each modality maps to one or more common latent dimensions, which can be interpreted using generative models. These latent dimensions, or "topics," provide a sparse summary of the generative process behind the features for each individual. Topic modeling, an unsupervised generative model, has been used to map seemingly disparate features to a common domain. We use Non-Negative Matrix Factorization (NMF) to infer the latent structure of multimodal ADHD data containing fMRI, MRI, phenotypic and behavioral measurements. We compare four different NMF algorithms and find that the sparsest decomposition is also the most differentiating between ADHD and healthy patients. We identify dimensions that map to interpretable, recognizable dimensions such as motion, default mode network activity, and other such features of the input data. For example, structural and functional graph theory features related to default mode subnetworks clustered with the ADHD-Inattentive diagnosis. Structural measurements of the default mode network (DMN) regions such as the posterior cingulate, precuneus, and parahippocampal regions were all related to the ADHD-Inattentive diagnosis. Ventral DMN subnetworks may have more functional connections in ADHD-I, while dorsal DMN may have less. ADHD topics are dependent upon diagnostic site, suggesting diagnostic differences across geographic locations. We assess our findings in light of the ADHD-200 classification competition, and contrast our unsupervised, nominated topics with previously published supervised learning methods. Finally, we demonstrate the validity of these latent variables as biomarkers by using them for classification of ADHD in 730 patients. Cumulatively, this manuscript addresses how multimodal data in ADHD can be interpreted by latent dimensions.
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Nearly all early fusion papers performed numericalization of image features, converting them to structured data prior to processing, while two performed matrix factorization.
“Nearly all these papers performed numericalization of image features in essence converting them to structured data prior to processing, however, two performed matrix factorization34,36.”
From Multimodal machine learning in precision health: A scoping review (2022) · cites this paper as [34]
Of those that have, classifier performance tends to be poorer overall than for studies utilizing structural or functional data alone.
“Of those that have, classifier performance tends to be poorer overall than for studies utilizing structural or functional data alone.50, 51, 52, 53, 59”
From Progress and roadblocks in the search for brain-based biomarkers of autism and attention-deficit/hyperactivity disorder (2017) · cites this paper as [50]
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- Authors
- Anderson, A., Douglas, P. K., Kerr, W. T., Haynes, V. S., Yuille, A. L., Xie, J., Wu, Y. N., Brown, J. A., & Cohen, M. S.
- Affiliations
- University of California, Los Angeles (US)
- Eli Lilly (United States) (US)
- University of California, San Francisco (US)
- California NanoSystems Institute (US)
- Corresponding author
- Anderson, Ariana
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-11
- Volume
- 102
- Pages
- 207-219
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC4063903
- OpenAlex
- W2155830939
- Cited by
- 136
- References
- 73
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 6 · 2023: 8 · 2022: 12 · 2021: 16 · 2020: 20 · 2019: 12 · 2018: 10 · 2017: 14 · 2016: 7 · 2015: 11 · 2014: 7
- FWCI
- 1.14
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Janssen Research & Development
- Burroughs Wellcome Fund
- NSF (DMS-1007889)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Attention Deficit Hyperactivity Disorder
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Keywords
- attention deficit hyperactivity disorder, default mode network, non-negative matrix factorization, topic modeling, multimodal neuroimaging, graph theory, posterior cingulate cortex, precuneus, parahippocampal cortex, ADHD classification, ADHD-200
- MeSH terms
- Adolescent, Algorithms, Attention Deficit Disorder with Hyperactivity, Child, Female, Humans, Magnetic Resonance Imaging, Male, Phenotype, Young Adult, Neuroimaging, Multimodal Imaging
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