Chained regularization for identifying brain patterns specific to HIV infection

• Published in: NeuroImage (Orlando, Fla.) Vol. 183; pp. 425 - 437
• Main Authors: Adeli, Ehsan, Kwon, Dongjin, Zhao, Qingyu, Pfefferbaum, Adolf, Zahr, Natalie M., Sullivan, Edith V., Pohl, Kilian M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.12.2018; Elsevier Limited
• Subjects: Accuracy; Adult; Aging; Antiretroviral therapy; Artificial intelligence; Brain; Brain - diagnostic imaging; Brain - pathology; Classification; Cognition; Computational neuroscience; Cortex; Drug therapy; Female; Group sparsity; HIV; HIV Infections - diagnostic imaging; HIV Infections - pathology; Human immunodeficiency virus; Human immunodeficiency virus (HIV); Humans; Image Processing, Computer-Assisted - methods; Immune response; Infections; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Methods; Middle Aged; MRI brain image analysis; Multiple kernel learning; NMR; Norms; Nuclear magnetic resonance; Pattern Recognition, Automated - methods; Public health; Sparsity; Multiple kernel learning; Group sparsity; Computational neuroscience; MRI brain image analysis; Human immunodeficiency virus (HIV)
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2018.08.022

Human Immunodeficiency Virus (HIV) infection continues to have major adverse public health and clinical consequences despite the effectiveness of combination Antiretroviral Therapy (cART) in reducing HIV viral load and improving immune function. As successfully treated individuals with HIV infection age, their cognition declines faster than reported for normal aging. This phenomenon underlines the importance of improving long-term care, which requires a better understanding of the impact of HIV on the brain. In this paper, automated identification of patients and brain regions affected by HIV infection are modeled as a classification problem, whose solution is determined in two steps within our proposed Chained-Regularization framework. The first step focuses on selecting the HIV pattern (i.e., the most informative constellation of brain region measurements for distinguishing HIV infected subjects from healthy controls) by constraining the search for the optimal parameter setting of the classifier via group sparsity (ℓ2,1-norm). The second step improves classification accuracy by constraining the parameterization with respect to the selected measurements and the Euclidean regularization (ℓ2-norm). When applied to the cortical and subcortical structural Magnetic Resonance Images (MRI) measurements of 65 controls and 65 HIV infected individuals, this approach is more accurate in distinguishing the two cohorts than more common models. Finally, the brain regions of the identified HIV pattern concur with the HIV literature that uses traditional group analysis models.