Multimodal magnetic resonance neuroimaging measures characteristic of early \textlessspan style="font-variant:small-caps;"\textgreater\cART\\textless/span\textgreater‐treated pediatric \textlessspan style="font-variant:small-caps;"\textgreater\HIV\\textless/span\textgreater: \A\ feature selection approach. Khobo, I. L, Jankiewicz, M., Holmes, M. J, Little, F., Cotton, M. F, Laughton, B., Van Der Kouwe, A. J W, Moreau, A., Nwosu, E., Meintjes, E. M, & Robertson, F. C Human Brain Mapping, 43(13):4128–4144, sep, 2022. Paper doi abstract bibtex Abstract Children with perinatally acquired HIV (CPHIV) have poor cognitive outcomes despite early combination antiretroviral therapy (cART). While CPHIV‐related brain alterations can be investigated separately using proton magnetic resonance spectroscopy ( 1 H‐MRS), structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and functional MRI (fMRI), a set of multimodal MRI measures characteristic of children on cART has not been previously identified. We used the embedded feature selection of a logistic elastic‐net (EN) regularization to select neuroimaging measures that distinguish CPHIV from controls and measured their classification performance via the area under the receiver operating characteristic curve (AUC) using repeated cross validation. We also wished to establish whether combining MRI modalities improved the models. In single modality analysis, sMRI volumes performed best followed by DTI, whereas individual EN models on spectroscopic, gyrification, and cortical thickness measures showed no class discrimination capability. Adding DTI and 1 H‐MRS in basal measures to sMRI volumes produced the highest classification performance . The best multimodal MRI set consisted of 22 DTI and sMRI volume features, which included reduced volumes of the bilateral globus pallidus and amygdala, as well as increased mean diffusivity (MD) and radial diffusivity (RD) in the right corticospinal tract in cART‐treated CPHIV. Consistent with previous studies of CPHIV, select subcortical volumes obtained from sMRI provide reasonable discrimination between CPHIV and controls. This may give insight into neuroimaging measures that are relevant in understanding the effects of HIV on the brain, thereby providing a starting point for evaluating their link with cognitive performance in CPHIV.
@article{khobo_multimodal_2022,
abstract = {Abstract
Children with perinatally acquired HIV (CPHIV) have poor cognitive outcomes despite early combination antiretroviral therapy (cART). While CPHIV‐related brain alterations can be investigated separately using proton magnetic resonance spectroscopy (
1
H‐MRS), structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and functional MRI (fMRI), a set of multimodal MRI measures characteristic of children on cART has not been previously identified. We used the embedded feature selection of a logistic elastic‐net (EN) regularization to select neuroimaging measures that distinguish CPHIV from controls and measured their classification performance via the area under the receiver operating characteristic curve (AUC) using repeated cross validation. We also wished to establish whether combining MRI modalities improved the models. In single modality analysis, sMRI volumes performed best followed by DTI, whereas individual EN models on spectroscopic, gyrification, and cortical thickness measures showed no class discrimination capability. Adding DTI and
1
H‐MRS in basal measures to sMRI volumes produced the highest classification performance . The best multimodal MRI set consisted of 22 DTI and sMRI volume features, which included reduced volumes of the bilateral globus pallidus and amygdala, as well as increased mean diffusivity (MD) and radial diffusivity (RD) in the right corticospinal tract in cART‐treated CPHIV. Consistent with previous studies of CPHIV, select subcortical volumes obtained from sMRI provide reasonable discrimination between CPHIV and controls. This may give insight into neuroimaging measures that are relevant in understanding the effects of HIV on the brain, thereby providing a starting point for evaluating their link with cognitive performance in CPHIV.},
author = {Khobo, Isaac L and Jankiewicz, Marcin and Holmes, Martha J and Little, Francesca and Cotton, Mark F and Laughton, Barbara and {Van Der Kouwe}, Andre J W and Moreau, Allison and Nwosu, Emmanuel and Meintjes, Ernesta M and Robertson, Frances C},
doi = {10.1002/hbm.25907},
file = {:Users/jacquelinebracher/Zotero/storage/CFD5PJB5/Khobo et al. - 2022 - Multimodal magnetic resonance neuroimaging measure.pdf:pdf},
issn = {1065-9471, 1097-0193},
journal = {Human Brain Mapping},
month = {sep},
number = {13},
pages = {4128--4144},
shorttitle = {Multimodal magnetic resonance neuroimaging measure},
title = {{Multimodal magnetic resonance neuroimaging measures characteristic of early {\textless}span style="font-variant:small-caps;"{\textgreater}{\{}cART{\}}{\textless}/span{\textgreater}‐treated pediatric {\textless}span style="font-variant:small-caps;"{\textgreater}{\{}HIV{\}}{\textless}/span{\textgreater}: {\{}A{\}} feature selection approach}},
url = {https://onlinelibrary.wiley.com/doi/10.1002/hbm.25907},
volume = {43},
year = {2022}
}
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While CPHIV‐related brain alterations can be investigated separately using proton magnetic resonance spectroscopy ( 1 H‐MRS), structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and functional MRI (fMRI), a set of multimodal MRI measures characteristic of children on cART has not been previously identified. We used the embedded feature selection of a logistic elastic‐net (EN) regularization to select neuroimaging measures that distinguish CPHIV from controls and measured their classification performance via the area under the receiver operating characteristic curve (AUC) using repeated cross validation. We also wished to establish whether combining MRI modalities improved the models. In single modality analysis, sMRI volumes performed best followed by DTI, whereas individual EN models on spectroscopic, gyrification, and cortical thickness measures showed no class discrimination capability. 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