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TOWARD A CONNECTIVITY GRADIENT-BASED FRAMEWORK FOR REPRODUCIBLE BIOMARKER DISCOVERY

View ORCID ProfileSeok-Jun Hong, Ting Xu, View ORCID ProfileAki Nikolaidis, View ORCID ProfileJonathan Smallwood, View ORCID ProfileDaniel S. Margulies, View ORCID ProfileBoris Bernhardt, View ORCID ProfileJoshua Vogelstein, View ORCID ProfileMichael P. Milham
doi: https://doi.org/10.1101/2020.04.15.043315
Seok-Jun Hong
1Center for the Developing Brain, Child Mind Institute, NY, USA
2Center for Neuroscience Imaging Research, Institute for Basic Science
3Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
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  • For correspondence: Michael.Milham@childmind.org Sukjun.Hong@childmind.org
Ting Xu
1Center for the Developing Brain, Child Mind Institute, NY, USA
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Aki Nikolaidis
1Center for the Developing Brain, Child Mind Institute, NY, USA
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Jonathan Smallwood
4Department of Psychology, University of York, Heslington, England, UK
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Daniel S. Margulies
5Frontlab, Institut du Cerveau et de la Moelle épinière, UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225, Paris, France
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Boris Bernhardt
6McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
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Joshua Vogelstein
7Department of Biomedical Engineering Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, Johns Hopkins University, MD, USA
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Michael P. Milham
8Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, NY, USA
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  • ORCID record for Michael P. Milham
  • For correspondence: Michael.Milham@childmind.org Sukjun.Hong@childmind.org
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Abstract

Despite myriad demonstrations of feasibility, the high dimensionality of fMRI data remains a critical barrier to its utility for reproducible biomarker discovery. Recent studies applying dimensionality reduction techniques to resting-state fMRI (R-fMRI) have unveiled neurocognitively meaningful connectivity gradients that are present in both human and primate brains, and appear to differ meaningfully among individuals and clinical populations. Here, we provide a critical assessment of the suitability of connectivity gradients for biomarker discovery. Using the Human Connectome Project (discovery subsample=209; two replication subsamples= 209×2) and the Midnight scan club (n=9), we tested the following key biomarker traits – reliability, reproducibility and predictive validity – of functional gradients. In doing so, we systematically assessed the effects of three analytical settings, including i) dimensionality reduction algorithms (i.e., linear vs. non-linear methods), ii) input data types (i.e., raw time series, [un-]thresholded functional connectivity), and iii) amount of the data (R-fMRI time-series lengths). We found that the reproducibility of functional gradients across algorithms and subsamples is generally higher for those explaining more variances of whole-brain connectivity data, as well as those having higher reliability. Notably, among different analytical settings, a linear dimensionality reduction (principal component analysis in our study), more conservatively thresholded functional connectivity (e.g., 95-97%) and longer time-series data (at least ≥20mins) was found to be preferential conditions to obtain higher reliability. Those gradients with higher reliability were able to predict unseen phenotypic scores with a higher accuracy, highlighting reliability as a critical prerequisite for validity. Importantly, prediction accuracy with connectivity gradients exceeded that observed with more traditional edge-based connectivity measures, suggesting the added value of a low-dimensional gradient approach. Finally, the present work highlights the importance and benefits of systematically exploring the parameter space for new imaging methods before widespread deployment.

Highlights

  • - There is a growing need to identify benchmark parameters in advancing functional connectivity gradients into a reliable biomarker.

  • - Here, we explored multidimensional parameter space in calculating functional gradients to improve their reproducibility, reliability and predictive validity.

  • - We demonstrated that more reproducible and reliable gradient markers tend to have higher predictive power for unseen phenotypic scores across various cognitive domains.

  • - We showed that the low-dimensional connectivity gradient approach could outperform raw edge-based analyses in terms of predicting phenotypic scores.

  • - We highlight the necessity of optimizing parameters for new imaging methods before their widespread deployment.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted April 17, 2020.
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TOWARD A CONNECTIVITY GRADIENT-BASED FRAMEWORK FOR REPRODUCIBLE BIOMARKER DISCOVERY
Seok-Jun Hong, Ting Xu, Aki Nikolaidis, Jonathan Smallwood, Daniel S. Margulies, Boris Bernhardt, Joshua Vogelstein, Michael P. Milham
bioRxiv 2020.04.15.043315; doi: https://doi.org/10.1101/2020.04.15.043315
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TOWARD A CONNECTIVITY GRADIENT-BASED FRAMEWORK FOR REPRODUCIBLE BIOMARKER DISCOVERY
Seok-Jun Hong, Ting Xu, Aki Nikolaidis, Jonathan Smallwood, Daniel S. Margulies, Boris Bernhardt, Joshua Vogelstein, Michael P. Milham
bioRxiv 2020.04.15.043315; doi: https://doi.org/10.1101/2020.04.15.043315

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