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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Exploring Automated Machine Learning for Cognitive Outcome Prediction from Multimodal Brain Imaging using STREAMLINE.

Xinkai Wang¹, Yanbo Feng¹, Boning Tong¹

¹University of Pennsylvania, Philadelphia, PA.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science

|June 23, 2023

Summary

STREAMLINE now supports regression analysis for machine learning (ML). This enhanced AutoML pipeline effectively predicts Alzheimer's disease (AD) outcomes using brain imaging data and identifies biomarkers.

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Area of Science:

Neuroscience
Computer Science
Biomedical Engineering

Background:

Automated machine learning (AutoML) pipelines simplify complex modeling.
Existing AutoML tools often lack comprehensive regression capabilities.
Predicting Alzheimer's disease (AD) cognitive outcomes requires advanced analytical methods.

Purpose of the Study:

To extend the STREAMLINE AutoML pipeline with regression-based machine learning models.
To evaluate the pipeline's effectiveness in predicting Alzheimer's disease (AD) cognitive outcomes.
To identify multimodal brain imaging biomarkers for AD.

Main Methods:

Implementation of multiple regression models within STREAMLINE: linear regression, elastic net, group lasso, and L21 norm.
Application of the enhanced STREAMLINE pipeline to multimodal brain imaging data for AD prediction.
Empirical validation of the pipeline's performance and biomarker discovery capabilities.

Main Results:

The extended STREAMLINE pipeline successfully incorporates regression-based machine learning models.
The pipeline demonstrates feasibility and effectiveness in predicting AD cognitive outcomes.
Multimodal brain imaging biomarkers for AD were successfully discovered using the STREAMLINE pipeline.

Conclusions:

The expanded STREAMLINE AutoML pipeline offers a robust solution for regression analysis in machine learning.
This tool facilitates the evaluation of Alzheimer's disease (AD) regression models.
STREAMLINE aids in the discovery of novel multimodal imaging biomarkers for AD.