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Investigating fMRI Preprocessing Variations: Multi-echo ICA vs. Conventional Preprocessing (DSI-SRP)

Posted by on Sunday, August 15, 2021 in College of Arts and Science, Completed Research, DSI-SRP, DSI-Supported Research, Engineering, School of Engineering, Social and Behavioral Sciences.

This DSI-SRP fellowship funded Hangling Liu to work in the lab of Catie Chang during the summer of 2021. Hangling is a Junior with majors in Computer Science and Economics and a minor in Spanish.

Hangling worked in the Neuroimaging & Brain Dynamics Lab during the summer of 2021, and focused on computational analyses and image data processing with the ACCRE high-performance computing cluster. ACCRE utilizes many computers that are networked together to perform intensive computing jobs. Hangling investigated functional magnetic resonance imaging (fMRI) images, specifically, translating fMRI pre-processing pipelines to be run through ACCRE as well as building new pipelines for the lab to utilize. One of the pipelines She worked to implement is a denoising pipeline (called ICA-AROMA), which uses a signal processing technique called Independent Component Analysis (ICA) to identify and reduce artifacts including head motion, cardiac pulsatility, and respiration effects.

Matlab programming and Linux shell scripting were used to carry out these steps. Hangling was able to modify the existing pipeline to provide the appropriate inputs to ICA-AROMA. Our next steps would be to evaluate these data processing pipelines in terms of their ability to improve the correspondence between fMRI and neuronal signals, as assessed from simultaneous EEG. The broader impact of this work is to contribute new techniques and knowledge that can improve the use of fMRI for measuring brain activity.

In addition to receiving support through a DSI-SRP fellowship, this project was supported and facilitated by the DSI Data Science Team through their regular summer workshops and demo sessions.

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