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7 March 2022 Machine learning feature extraction in naturalistic stimuli for human brain mapping using high-density diffuse optical tomography
Morgan Fogarty, Kalyan Tripathy, Alexandra M. Svoboda, Mariel L. Schroeder, Sean Rafferty, Patricia Mansfield, Rachel Ulbrich, Madison Booth, Edward J. Richter, Christopher D. Smyser, Adam T. Eggebrecht, Joseph P. Culver
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Proceedings Volume PC11945, Clinical and Translational Neurophotonics 2022; PC1194508 (2022) https://doi.org/10.1117/12.2608946
Event: SPIE BiOS, 2022, San Francisco, California, United States
Abstract
Studying brain development requires child-friendly imaging modalities and stimulus paradigms. High density diffuse optical tomography provides enhanced image quality over fNIRS and is validated extensively against fMRI in adults. Movie viewing reduces head motion and increases task engagement. Movie features are tracked and correlated with brain activity to map multiple processing pathways in parallel. We propose machine learning methods to extract high-level audiovisual features to avoid the time-consuming, subjective task of manual coding these feature regressors. Using a Faster Region-based Convolutional Neural Network, we achieve high correlation values between manually and automatically generated face regressors and regression coefficient maps.
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Morgan Fogarty, Kalyan Tripathy, Alexandra M. Svoboda, Mariel L. Schroeder, Sean Rafferty, Patricia Mansfield, Rachel Ulbrich, Madison Booth, Edward J. Richter, Christopher D. Smyser, Adam T. Eggebrecht, and Joseph P. Culver "Machine learning feature extraction in naturalistic stimuli for human brain mapping using high-density diffuse optical tomography", Proc. SPIE PC11945, Clinical and Translational Neurophotonics 2022, PC1194508 (7 March 2022); https://doi.org/10.1117/12.2608946
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KEYWORDS
Brain mapping
Machine learning
Diffuse optical tomography
Feature extraction
Brain
Functional magnetic resonance imaging
Image quality
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