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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Multi-Label Transduction for Identifying Disease Comorbidity Patterns.

Ehsan Adeli¹, Dongjin Kwon^1,2, Kilian M Pohl²

¹Stanford University.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

|March 10, 2021

Summary

This summary is machine-generated.

A new multi-label transductive method accurately identifies brain changes in individuals with Alcohol Use Disorder (AUD) and HIV infection. This approach effectively models disease comorbidity, revealing unique patterns for each condition and their combined effects.

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Area of Science:

Neuroimaging
Computational Biology
Medical Informatics

Background:

Assessing the impact of comorbid diseases on brain morphology is challenging with traditional classification methods.
Binary or multi-class classifiers struggle with complex disorders and overlapping disease labels, leading to inconclusive findings.
Multi-label classification offers a more suitable framework for modeling disease comorbidity.

Purpose of the Study:

To develop and evaluate a novel multi-label transductive (MLT) method for analyzing brain morphology in comorbid conditions.
To accurately classify subjects with Alcohol Use Disorder (AUD), human immunodeficiency virus (HIV) infection, and their comorbidity using MRI data.
To identify unique neuroimaging patterns associated with individual diseases and their combined effects.

Main Methods:

Proposed a multi-label transductive (MLT) method based on low-rank matrix completion.
Applied the MLT method to a dataset of healthy controls, AUD, HIV, and AUD+HIV individuals.
Compared the MLT method's accuracy against conventional classification approaches.

Main Results:

The proposed MLT method demonstrated higher accuracy in subject classification compared to common methods.
The MLT approach successfully identified distinct neuroimaging patterns for AUD, HIV, and the AUD+HIV comorbidity.
Findings suggest that the AUD+HIV comorbidity exhibits a compounding effect of both diseases on brain morphology.

Conclusions:

The MLT method provides an effective approach for studying brain morphology in the context of complex disease comorbidities.
This technique enhances diagnostic accuracy and reveals disease-specific and comorbid-specific neuroimaging signatures.
The study highlights the compounded impact of AUD and HIV on the brain, underscoring the need for tailored diagnostic and treatment strategies.