Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Deep Neural Networks and Kernel Regression Achieve Comparable Accuracies for Functional Connectivity Prediction of Behavior and Demographics

View ORCID ProfileTong He, View ORCID ProfileRu Kong, View ORCID ProfileAvram J. Holmes, View ORCID ProfileMinh Nguyen, View ORCID ProfileMert R. Sabuncu, View ORCID ProfileSimon B. Eickhoff, View ORCID ProfileDanilo Bzdok, View ORCID ProfileJiashi Feng, View ORCID ProfileB.T. Thomas Yeo
doi: https://doi.org/10.1101/473603
Tong He
Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, SingaporeDepartment of Electrical and Computer Engineering, National University of Singapore, Singapore
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Tong He
Ru Kong
Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, SingaporeDepartment of Electrical and Computer Engineering, National University of Singapore, Singapore
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Ru Kong
Avram J. Holmes
Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Avram J. Holmes
Minh Nguyen
Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, SingaporeDepartment of Electrical and Computer Engineering, National University of Singapore, Singapore
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Minh Nguyen
Mert R. Sabuncu
School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Mert R. Sabuncu
Simon B. Eickhoff
Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, GermanyInstitute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Simon B. Eickhoff
Danilo Bzdok
Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, GermanyJARA-BRAIN, Jülich-Aachen Research Alliance, GermanyParietal team, INRIA, Neurospin, bat 145, CEA Saclay, 91191 Gif-sur-Yvette, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Danilo Bzdok
Jiashi Feng
Department of Electrical and Computer Engineering, National University of Singapore, Singapore
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Jiashi Feng
B.T. Thomas Yeo
Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, SingaporeDepartment of Electrical and Computer Engineering, National University of Singapore, SingaporeMartinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USACentre for Cognitive Neuroscience, Duke-NUS Medical School, SingaporeNUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for B.T. Thomas Yeo
  • For correspondence: thomas.yeo@nus.edu.sg
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted July 22, 2019.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Deep Neural Networks and Kernel Regression Achieve Comparable Accuracies for Functional Connectivity Prediction of Behavior and Demographics
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
Share
Deep Neural Networks and Kernel Regression Achieve Comparable Accuracies for Functional Connectivity Prediction of Behavior and Demographics
Tong He, Ru Kong, Avram J. Holmes, Minh Nguyen, Mert R. Sabuncu, Simon B. Eickhoff, Danilo Bzdok, Jiashi Feng, B.T. Thomas Yeo
bioRxiv 473603; doi: https://doi.org/10.1101/473603
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
Deep Neural Networks and Kernel Regression Achieve Comparable Accuracies for Functional Connectivity Prediction of Behavior and Demographics
Tong He, Ru Kong, Avram J. Holmes, Minh Nguyen, Mert R. Sabuncu, Simon B. Eickhoff, Danilo Bzdok, Jiashi Feng, B.T. Thomas Yeo
bioRxiv 473603; doi: https://doi.org/10.1101/473603

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (1641)
  • Biochemistry (2721)
  • Bioengineering (1902)
  • Bioinformatics (10202)
  • Biophysics (4174)
  • Cancer Biology (3202)
  • Cell Biology (4522)
  • Clinical Trials (135)
  • Developmental Biology (2831)
  • Ecology (4447)
  • Epidemiology (2041)
  • Evolutionary Biology (7213)
  • Genetics (5464)
  • Genomics (6795)
  • Immunology (2379)
  • Microbiology (7462)
  • Molecular Biology (2978)
  • Neuroscience (18529)
  • Paleontology (135)
  • Pathology (472)
  • Pharmacology and Toxicology (776)
  • Physiology (1147)
  • Plant Biology (2692)
  • Scientific Communication and Education (679)
  • Synthetic Biology (885)
  • Systems Biology (2840)
  • Zoology (465)