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Investigation of brain ageing in HIV-positive individuals using convolutional neural networks
Developments in the field of Deep Learning (DL) have provided new means of tracking healthy ageing, and have established DL-predicted brain age as an accurate and reliable biomarker for brain health. Deviations from a healthy brain ageing trajectory, indicated by an increased predicted brain age rel...
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| Format: | Thesis |
| Language: | English |
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Department of Mathematics and Applied Mathematics
2025
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| _version_ | 1867613312882049024 |
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| access_status_str | Open Access |
| author | Catzel, Rachel |
| author2 | Shock, Jonathan |
| author_browse | Catzel, Rachel Shock, Jonathan |
| author_facet | Shock, Jonathan Catzel, Rachel |
| author_sort | Catzel, Rachel |
| collection | Thesis |
| description | Developments in the field of Deep Learning (DL) have provided new means of tracking healthy ageing, and have established DL-predicted brain age as an accurate and reliable biomarker for brain health. Deviations from a healthy brain ageing trajectory, indicated by an increased predicted brain age relative to chronological age, and thus positive brain age delta, have been associated with cognitive impairments. This thesis focuses on de veloping a robust brain age prediction model to investigate brain ageing in HIV-positive individuals. We utilise the UK Biobank, CamCAN, and ENIGMA-HIV datasets for this task and train a convolutional neural network in two stages. First, we pre-train the model on the large UK Biobank dataset (N=21366) which contains individuals in the age range of 45-82 years. To this end, we achieve a mean absolute error (MAE) of 2.57±1.94 years. Next, we fine-tune the pre-trained model on a smaller dataset, with a wider age range, aligned with that of our testing dataset from ENIGMA-HIV. We select the CamCAN dataset (N=484) for this, with individuals spanning the age range of 18-88 years. We obtain an MAE of 3.54 ± 2.59 years on the holdout CamCAN test set, substantially im proving upon the 6.38 ± 5.30 years MAE achieved without pre-training. We then apply the trained model to the multi-site ENIGMA-HIV testing dataset which we have har monised to remove inter-site variation. Following testing, we apply a fixed-effects model to analyse whether the brain age deltas are significantly higher in HIV-positive individu als compared to HIV-negative controls. Although no statistically significant difference is found in the brain age deltas due to HIV status, further analysis reveals significant cor relations between the brain age deltas and specific HIV clinical measures, in particular, nadir CD4 count and current CD4 count. This thesis's findings contribute to under standing the impact of HIV on brain ageing and associated factors of significance, and highlights the value of DL techniques in medical research. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/40822 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:34:08.683Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Department of Mathematics and Applied Mathematics |
| publisherStr | Department of Mathematics and Applied Mathematics |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/40822 Investigation of brain ageing in HIV-positive individuals using convolutional neural networks Catzel, Rachel Shock, Jonathan Moodley, Deshendran Applied mathematics Developments in the field of Deep Learning (DL) have provided new means of tracking healthy ageing, and have established DL-predicted brain age as an accurate and reliable biomarker for brain health. Deviations from a healthy brain ageing trajectory, indicated by an increased predicted brain age relative to chronological age, and thus positive brain age delta, have been associated with cognitive impairments. This thesis focuses on de veloping a robust brain age prediction model to investigate brain ageing in HIV-positive individuals. We utilise the UK Biobank, CamCAN, and ENIGMA-HIV datasets for this task and train a convolutional neural network in two stages. First, we pre-train the model on the large UK Biobank dataset (N=21366) which contains individuals in the age range of 45-82 years. To this end, we achieve a mean absolute error (MAE) of 2.57±1.94 years. Next, we fine-tune the pre-trained model on a smaller dataset, with a wider age range, aligned with that of our testing dataset from ENIGMA-HIV. We select the CamCAN dataset (N=484) for this, with individuals spanning the age range of 18-88 years. We obtain an MAE of 3.54 ± 2.59 years on the holdout CamCAN test set, substantially im proving upon the 6.38 ± 5.30 years MAE achieved without pre-training. We then apply the trained model to the multi-site ENIGMA-HIV testing dataset which we have har monised to remove inter-site variation. Following testing, we apply a fixed-effects model to analyse whether the brain age deltas are significantly higher in HIV-positive individu als compared to HIV-negative controls. Although no statistically significant difference is found in the brain age deltas due to HIV status, further analysis reveals significant cor relations between the brain age deltas and specific HIV clinical measures, in particular, nadir CD4 count and current CD4 count. This thesis's findings contribute to under standing the impact of HIV on brain ageing and associated factors of significance, and highlights the value of DL techniques in medical research. 2025-01-22T19:43:00Z 2025-01-22T19:43:00Z 2024 2025-01-22T19:01:40Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/40822 eng application/pdf Department of Mathematics and Applied Mathematics Faculty of Science University of Cape Town |
| spellingShingle | Applied mathematics Catzel, Rachel Investigation of brain ageing in HIV-positive individuals using convolutional neural networks |
| thesis_degree_str | Master's |
| title | Investigation of brain ageing in HIV-positive individuals using convolutional neural networks |
| title_full | Investigation of brain ageing in HIV-positive individuals using convolutional neural networks |
| title_fullStr | Investigation of brain ageing in HIV-positive individuals using convolutional neural networks |
| title_full_unstemmed | Investigation of brain ageing in HIV-positive individuals using convolutional neural networks |
| title_short | Investigation of brain ageing in HIV-positive individuals using convolutional neural networks |
| title_sort | investigation of brain ageing in hiv positive individuals using convolutional neural networks |
| topic | Applied mathematics |
| url | http://hdl.handle.net/11427/40822 |
| work_keys_str_mv | AT catzelrachel investigationofbrainageinginhivpositiveindividualsusingconvolutionalneuralnetworks |