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Chained regularization for identifying brain patterns specific to HIV infection.

Ehsan Adeli1, Dongjin Kwon2, Qingyu Zhao1

  • 1Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, 94305, USA.

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|August 24, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Chained-Regularization framework to accurately identify brain changes in individuals with Human Immunodeficiency Virus (HIV) infection. The method enhances understanding of HIV

Keywords:
Computational neuroscienceGroup sparsityHuman immunodeficiency virus (HIV)MRI brain image analysisMultiple kernel learning

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Machine Learning

Background:

  • Human Immunodeficiency Virus (HIV) infection poses long-term risks, including accelerated cognitive decline in aging individuals despite effective combination Antiretrotroviral Therapy (cART).
  • Understanding HIV's impact on the brain is crucial for improving long-term care and patient outcomes.
  • Existing methods may not fully capture the complex neurological alterations associated with HIV.

Purpose of the Study:

  • To develop and validate an automated classification framework for identifying HIV-associated brain changes.
  • To pinpoint specific brain regions and patterns indicative of HIV infection using structural MRI data.
  • To enhance the accuracy of distinguishing HIV-infected individuals from healthy controls.

Main Methods:

  • A two-step Chained-Regularization framework was employed for automated classification.
  • Step 1 utilized group sparsity (ℓ2,1-norm) to identify informative brain region patterns (HIV pattern).
  • Step 2 applied Euclidean regularization (ℓ2-norm) to refine classification accuracy based on selected measurements.
  • The model was tested on structural Magnetic Resonance Images (MRI) from 65 HIV-infected individuals and 65 controls.

Main Results:

  • The proposed Chained-Regularization framework demonstrated superior accuracy in distinguishing HIV-infected individuals from healthy controls compared to conventional models.
  • The identified HIV pattern, highlighting specific brain regions, aligns with findings from traditional neuroimaging group analysis studies.
  • The automated approach effectively leverages cortical and subcortical MRI measurements.

Conclusions:

  • The Chained-Regularization framework offers a robust and accurate method for automated identification of HIV-related brain alterations.
  • This approach contributes to a better understanding of the neurological impact of HIV, supporting improved clinical care and research.
  • The findings validate the framework's ability to identify clinically relevant brain patterns associated with HIV infection.