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Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets

  • Pradyumna Lanka
  • D Rangaprakash
  • Michael N. Dretsch
  • Jeffrey S. Katz
  • Thomas S. DenneyJr
  • Gopikrishna DeshpandeEmail author
Original Research

Abstract

There are growing concerns about the generalizability of machine learning classifiers in neuroimaging. In order to evaluate this aspect across relatively large heterogeneous populations, we investigated four disorders: Autism spectrum disorder (N = 988), Attention deficit hyperactivity disorder (N = 930), Post-traumatic stress disorder (N = 87) and Alzheimer’s disease (N = 132). We applied 18 different machine learning classifiers (based on diverse principles) wherein the training/validation and the hold-out test data belonged to samples with the same diagnosis but differing in either the age range or the acquisition site. Our results indicate that overfitting can be a huge problem in heterogeneous datasets, especially with fewer samples, leading to inflated measures of accuracy that fail to generalize well to the general clinical population. Further, different classifiers tended to perform well on different datasets. In order to address this, we propose a consensus-classifier by combining the predictive power of all 18 classifiers. The consensus-classifier was less sensitive to unmatched training/validation and holdout test data. Finally, we combined feature importance scores obtained from all classifiers to infer the discriminative ability of connectivity features. The functional connectivity patterns thus identified were robust to the classification algorithm used, age and acquisition site differences, and had diagnostic predictive ability in addition to univariate statistically significant group differences between the groups. A MATLAB toolbox called Machine Learning in NeuroImaging (MALINI), which implements all the 18 different classifiers along with the consensus classifier is available from Lanka et al. (2019) The toolbox can also be found at the following URL: https://github.com/pradlanka/malini.

Keywords

Resting-state functional MRI Supervised machine learning Diagnostic classification Functional connectivity Autism ADHD Alzheimer’s disease PTSD 

Notes

Acknowledgements

Attention deficit hyperactivity disorder (ADHD) data acquisition was supported by NIMH (National Institute of Mental Health, Bethesda, MD, USA) grant # R03MH096321. Alzheimer’s disease neuroimaging initiative (ADNI) data acquisition was funded by multiple agencies and the list can be obtained from http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. Autism brain imaging data exchange (ABIDE) data acquisition was supported by NIMH grant # K23MH087770. The authors would also like to thank the personnel at the traumatic brain injury (TBI) clinic and behavioral health clinic, Fort Benning, GA, USA and the US Army Aeromedical Research Laboratory, Fort Rucker, AL, USA, and most of all, the Soldiers who participated in the study. The authors thank Julie Rodiek and Wayne Duggan for facilitating post-traumatic stress disorder (PTSD) data acquisition.

Funding information

The authors acknowledge financial support for PTSD/PCS data acquisition from the U.S. Army Medical Research and Material Command (MRMC) (Grant # 00007218). The views, opinions, and/or findings from PTSD/PCS data contained in this article are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied, of the Department of Defense (DoD) or the United States Government.

Compliance with ethical standards

Conflict of interest

The authors declare that the research was conducted in the absence of any competing interests.

Ethical approval

This paper uses subject data from the publicly available databases such as ABIDE, ADHD-200 and ADNI. The data collection procedures for the participant’s neuroimaging data present in these databases was approved by the local Institutional Review Boards of the respective data acquisition sites. The data for military veterans with PCS/PTSD and controls was acquired at Auburn University. The procedure and the protocols in this study were approved by the Auburn University Institutional Review Board (IRB) and the Headquarters U.S. Army Medical Research and Material Command, IRB (HQ USAMRMC IRB). Material has been reviewed by the Walter Reed Army Institute of Research. There is no objection to its presentation and/or publication. The opinions or assertions contained herein are the private views of the author, and are not to be construed as official, or as reflecting true views of the Department of the Army or the Department of Defense. The investigators have adhered to the policies for protection of human subjects as prescribed in AR 70–25.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.AU MRI Research Center, Department of Electrical and Computer EngineeringAuburn UniversityAuburnUSA
  2. 2.Department of Psychological SciencesUniversity of California MercedMercedUSA
  3. 3.Departments of Radiology and Biomedical EngineeringNorthwestern UniversityChicagoUSA
  4. 4.U.S. Army Aeromedical Research LaboratoryFort RuckerUSA
  5. 5.US Army Medical Research Directorate-WestWalter Reed Army Institute for ResearchJoint Base Lewis-McCordUSA
  6. 6.Department of PsychologyAuburn UniversityAuburnUSA
  7. 7.Alabama Advanced Imaging ConsortiumBirminghamUSA
  8. 8.Center for NeuroscienceAuburn UniversityAuburnUSA
  9. 9.Center for Health Ecology and Equity ResearchAuburn UniversityAuburnUSA
  10. 10.Department of PsychiatryNational Institute of Mental and NeurosciencesBangaloreIndia

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