About this Research Topic
It is estimated that 37.7 million (30.2-45.1 million) people worldwide are living with HIV by the end of 2020, and a substantial proportion of them are accompanied by HIV-associated neurocognitive disorders (HAND). HAND refers to a spectrum of neurocognitive dysfunction associated with HIV infection possibly due to invasion of the central nervous system in the early stages of infection and inflammation in the later stages if infection persists. HAND significantly affects the cognitive functions of patients and places a great burden on the patients themselves and their families. HAND has been divided into three stages: symptomatic neurocognitive impairment (ANI), moderate cognitive impairment (MCI), and advanced HIV-associated dementia (HAD). With the introduction of combination antiretroviral therapy (cART), the incidence of HAD declined significantly, but ANI and MCI were still prevalent. Reports indicate that ANI has a high risk of rapidly evolving to HAD and that this evolution could be reversed with reliable treatment. Therefore, it is of great importance to detect the biomarkers for ANI as early as possible during HIV progression.
Non-invasive imaging techniques have long been used to detect subtle changes in the early stages of HAND and researchers have been able to detect a number of changes in key brain regions such as the cortical and subcortical regions. Applying recent technologies such as Artificial Intelligence, radiomics and deep learning to data from neuroimaging studies, including diffusion tensor imaging (DTI), functional magnetic resonance imaging (fMRI), and genetic, clinical and immunohistochemical studies, could provide new insights to detect and localise effective biomarkers, especially in future studies.
This Research Topic aims to provide a comprehensive collection of Original Research and Case Report on imaging studies of neural damage and subsequent neurocognitive disorders caused by HIV, especially diagnostic biomarkers for early stages HAND. Contributions should include the following technologies and any new theories and concepts to support HAND diagnoses and search for mechanisms:
- Diagnostic imaging, including computed tomography (CT), MR imaging, Digital Radiography, positron emission tomography (PET), computed tomography (CT), ultrasound imaging, digital subtraction angiography (DSA);
- Molecular imaging;
- Application of Artificial Intelligence and, Radiomics, and deep learning in image processing;
- Structural and functional network analysis in HIV-induced nerve damage.
Non-invasive imaging techniques have long been used to detect subtle changes in the early stages of HAND and researchers have been able to detect a number of changes in key brain regions such as the cortical and subcortical regions. Applying recent technologies such as Artificial Intelligence, radiomics and deep learning to data from neuroimaging studies, including diffusion tensor imaging (DTI), functional magnetic resonance imaging (fMRI), and genetic, clinical and immunohistochemical studies, could provide new insights to detect and localise effective biomarkers, especially in future studies.
This Research Topic aims to provide a comprehensive collection of Original Research and Case Report on imaging studies of neural damage and subsequent neurocognitive disorders caused by HIV, especially diagnostic biomarkers for early stages HAND. Contributions should include the following technologies and any new theories and concepts to support HAND diagnoses and search for mechanisms:
- Diagnostic imaging, including computed tomography (CT), MR imaging, Digital Radiography, positron emission tomography (PET), computed tomography (CT), ultrasound imaging, digital subtraction angiography (DSA);
- Molecular imaging;
- Application of Artificial Intelligence and, Radiomics, and deep learning in image processing;
- Structural and functional network analysis in HIV-induced nerve damage.
Keywords: HIV-associated dementia, HIV-associated neurocognitive disorder, imaging technique, Artificial Intelligence, Diagnostic imaging, Molecular imaging
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