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Ensemble learning with 3D convolutional neural networks for functional connectome-based prediction.

Meenakshi Khosla1, Keith Jamison2, Amy Kuceyeski3

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|June 21, 2019
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Brain parcellation affects resting-state fMRI (rs-fMRI) analysis. Stochastic parcellations perform comparably to atlases, leading to a novel ensemble learning strategy using 3D Convolutional Neural Networks (CNNs) for improved rs-fMRI predictions.

Keywords:
ABIDEAutism spectrum disorderConvolutional neural networksFunctional connectivityfMRI

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Area of Science:

  • Neuroimaging
  • Machine Learning
  • Computational Neuroscience

Background:

  • Resting-state functional MRI (rs-fMRI) analysis sensitivity relies on preprocessing, particularly brain parcellation for defining regions of interest (ROIs).
  • Evaluating the impact of different brain parcellation strategies on machine learning model performance in rs-fMRI is crucial for reliable data interpretation.

Purpose of the Study:

  • To critically assess how various brain parcellation schemes influence machine learning models applied to rs-fMRI data.
  • To introduce and validate an ensemble learning strategy combined with a novel 3D Convolutional Neural Network (CNN) for enhanced rs-fMRI analysis.

Main Methods:

  • Comparison of machine learning model performance using stochastic versus traditional atlas-based brain parcellations at equivalent spatial scales.
  • Development and implementation of an ensemble learning framework utilizing predictions from models trained on connectivity data from diverse parcellations.
  • Application of a novel 3D CNN approach to leverage full-resolution rs-fMRI data and model non-linear relationships.

Main Results:

  • Stochastic parcellations demonstrated performance comparable to widely-used atlases across models at similar spatial resolutions.
  • The proposed ensemble CNN framework effectively integrated predictions from multiple parcellations, outperforming traditional connectome models.
  • Promising results were achieved in both a classification task (autism vs. healthy controls) and a regression task (age prediction).

Conclusions:

  • Brain parcellation choice significantly impacts rs-fMRI machine learning outcomes, with stochastic methods offering competitive performance.
  • Ensemble learning, particularly with a 3D CNN, provides a robust framework to harness diverse parcellation information and improve predictive accuracy in rs-fMRI.
  • The developed approach offers a promising advancement for analyzing complex rs-fMRI data, overcoming limitations of traditional region-based and linear models.