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Confounding Effects on the Performance of Machine Learning Analysis of Static Functional Connectivity Computed from rs-fMRI Multi-site Data

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Abstract

Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive imaging technique widely used in neuroscience to understand the functional connectivity of the human brain. While rs-fMRI multi-site data can help to understand the inner working of the brain, the data acquisition and processing of this data has many challenges. One of the challenges is the variability of the data associated with different acquisitions sites, and different MRI machines vendors. Other factors such as population heterogeneity among different sites, with variables such as age and gender of the subjects, must also be considered. Given that most of the machine-learning models are developed using these rs-fMRI multi-site data sets, the intrinsic confounding effects can adversely affect the generalizability and reliability of these computational methods, as well as the imposition of upper limits on the classification scores. This work aims to identify the phenotypic and imaging variables producing the confounding effects, as well as to control these effects. Our goal is to maximize the classification scores obtained from the machine learning analysis of the Autism Brain Imaging Data Exchange (ABIDE) rs-fMRI multi-site data. To achieve this goal, we propose novel methods of stratification to produce homogeneous sub-samples of the 17 ABIDE sites, as well as the generation of new features from the static functional connectivity values, using multiple linear regression models, ComBat harmonization models, and normalization methods. The main results obtained with our statistical models and methods are an accuracy of 76.4%, sensitivity of 82.9%, and specificity of 77.0%, which are 8.8%, 20.5%, and 7.5% above the baseline classification scores obtained from the machine learning analysis of the static functional connectivity computed from the ABIDE rs-fMRI multi-site data.

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Availability of Data

Data available on http://preprocessed-connectomes-project.org/abide/.

Code Availability

The code used to compute the results presented in this paper available on https://github.com/pcdslab/ASD-DiagNet-Confounds.

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Funding

This research is funded by National Science Foundation (NSF) award No. TI-2213951. In addition, part of this research is funded by supplemental grant to NIH NIGMS R01GM134384. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author (s) and do not necessarily reflect the views of the National Science Foundation (NSF) or National Institutes of Health (NIH).

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Authors and Affiliations

  1. Knight Foundation School of Computing and Information Sciences, Florida International University, 11200 SW 8th Street CASE 354, Miami, Florida, 33199, USA

    Oswaldo Artiles & Fahad Saeed

  2. SUPMICROTECH, CNRS, institut FEMTO-ST, 24 rue Alain Savary, Besançon, F-25000, France

    Zeina Al Masry

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O.A.- Involved in the conception, design, writing code, computation and analysis of results, and writing the first draft of the manuscript; Z.A.M. Involved in the conception, design, analysis of results, and revising the manuscript; F.S. - Involved in the conception, design, analysis of results, and revising the manuscript. All authors read and approved the final manuscript.

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Correspondence to Fahad Saeed.

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Artiles, O., Al Masry, Z. & Saeed, F. Confounding Effects on the Performance of Machine Learning Analysis of Static Functional Connectivity Computed from rs-fMRI Multi-site Data. Neuroinform 21, 651–668 (2023). https://doi.org/10.1007/s12021-023-09639-1

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