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Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics
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Abstract
There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts in a variety of neuroimaging applications, yet there are few direct comparisons of relative utility. Here, we compared the performance of three DNN architectures and a classical machine learning algorithm (kernel regression) in predicting individual phenotypes from whole-brain resting-state functional connectivity (RSFC) patterns. One of the DNNs was a generic fully-connected feedforward neural network, while the other two DNNs were recently published approaches specifically designed to exploit the structure of connectome data. By using a combined sample of almost 10,000 participants from the Human Connectome Project (HCP) and UK Biobank, we showed that the three DNNs and kernel regression achieved similar performance across a wide range of behavioral and demographic measures. Furthermore, the generic feedforward neural network exhibited similar performance to the two state-of-the-art connectome-specific DNNs. When predicting fluid intelligence in the UK Biobank, performance of all algorithms dramatically improved when sample size increased from 100 to 1000 subjects. Improvement was smaller, but still significant, when sample size increased from 1000 to 5000 subjects. Importantly, kernel regression was competitive across all sample sizes. Overall, our study suggests that kernel regression is as effective as DNNs for RSFC-based behavioral prediction, while incurring significantly lower computational costs. Therefore, kernel regression might serve as a useful baseline algorithm for future studies.
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4 of the 124 references couldn’t be matched to a known paper.
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- Maas, 2013, Rectifier nonlinearities improve neural network acoustic models, vol. 28, 6
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Accurate brain age prediction with lightweight deep neural networks
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Self-citationOptimising network modelling methods for fMRI
Pervaiz, U., Vidaurre, D., Woolrich, M. W., & Smith, S. M. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116604 · Cited by 236
Metadata
- Authors
- He, T., Kong, R., Holmes, A. J., Nguyen, M., Sabuncu, M. R., Eickhoff, S. B., Bzdok, D., Feng, J., & Yeo, B. T. T.
- Affiliations
- National University of Singapore (SG)
- Yale University (US)
- Cornell University (US)
- Forschungszentrum Jülich (DE)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Corresponding author
- Yeo, B.T. Thomas
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 206
- Pages
- 116276
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC6984975
- OpenAlex
- W2979650665
- Cited by
- 271
- References
- 124
- Citations by year
- 2026: 19 · 2025: 46 · 2024: 46 · 2023: 41 · 2022: 47 · 2021: 42 · 2020: 26
- FWCI
- 17.15
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion, cc-by-nc-nd)
- National University of Singapore (submittedVersion, cc-by-nc-nd)
- HAL (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Medical Research Council (CBRG/0088/2015)
- National University of Singapore (R185000271720, DPRT/944/09/14)
- National Institutes of Health
- Ministry of Education - Singapore (MOE2014-T2-2-016)
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital (1U54MH091657, 25163)
- National Research Foundation Singapore
- NIH Blueprint for Neuroscience Research
- McDonnell Center for Systems Neuroscience
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- deep neural network, kernel regression, resting-state functional connectivity, feedforward neural networks, behavior prediction, fluid intelligence, demographic prediction, sample size effects, computational cost, Human Connectome Project, UK Biobank
- MeSH terms
- Machine Learning, Deep Learning, Adult, Age Factors, Aged, Brain, Female, Humans, Image Interpretation, Computer-Assisted, Intelligence, Magnetic Resonance Imaging, Male, Middle Aged, Psychomotor Performance, Sex Factors, Neural Networks, Computer, Biological Specimen Banks, Young Adult, Connectome, Datasets as Topic
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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