Mengrui Shi
ABSTRACT
Purpose: This study investigates variations in brain functional connectivity and activation regions during the processing of three different tones (first tone, second tone, third tone) in both deaf and hearing children, employing deep learning methods. Additionally, the research aims to discern differences in these brain responses between deaf and typically developing children. Methods: The study involves five deaf children and two typically developing children as participants, with resting-state functional magnetic resonance imaging (fMRI) scans conducted using a functional MRI scanner. The research workflow includes data preprocessing and fMRI data analysis. Specifically, a one-dimensional convolutional network is employed to extract features and classify the input brain functional connectivity data. Results: The study finds that the one-dimensional convolutional network is well-suited for capturing local patterns within sequential data and effectively extracting information pertaining to connectivity patterns from the one-dimensional sequence of connection weights. This makes it a suitable choice for processing brain functional connectivity data, especially in the context of deaf children.
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