Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics

He, Tong; Kong, Ru; Holmes, Avram J.; Nguyen, Minh; Sabuncu, Mert R.; Eickhoff, Simon B.; Bzdok, Danilo; Feng, Jiashi; Yeo, B.T. Thomas

doi:10.1016/j.neuroimage.2019.116276

Cited by 271Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and Applications

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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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He, Tong, Kong, Ru, Holmes, Avram J., Nguyen, Minh, Sabuncu, Mert R., Eickhoff, Simon B., Bzdok, Danilo, Feng, Jiashi, Yeo, B.T. Thomas·2020·NeuroImage

DOI 10.1016/j.neuroimage.2019.116276PMC PMC6984975

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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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Is deep learning better than kernel regression for functional connectivity prediction of fluid intelligence?
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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: He, Tong
Kong, Ru
Holmes, Avram J.
Nguyen, Minh
Sabuncu, Mert R.
Eickhoff, Simon B.
Bzdok, Danilo
Feng, Jiashi
Yeo, B.T. Thomas
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2020-02
Volume: 206
Pages: 116276
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2019.116276
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: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
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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Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics

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