Surface based deep learning to define HIV-related brain aging. Included investigators

  • Bandt, Katie (PD/PI)
  • Besson, Pierre Alain (Co-PD/PI)
  • Ragin, Ann (Other)

Project: Research project

Project Details

Description

With prolonged survival duration from combination antiretroviral therapy (cART), the percentage of older adults living with HIV (PLWH) will continue to rise in coming years. Despite the viral suppression that can be achieved on cART, PLWH remain at considerable risk of brain injury and cognitive decline. Many lines of evidence indicate accelerated aging in HIV infection, thought to be driven by chronic immune activation. Determining HIV’s impact on the brain and its function has proven challenging due to difficulties evaluating individual differences in brain aging relative to chronological age in PLWH. This has been further hampered by the heterogeneities within the PLWH population, as well as those of the normal aging process. The purpose of this proposal is to advance our understanding of how HIV infection affects brain aging. In order to achieve this purpose, we propose to use our newly developed method, surface-based deep learning (SBDL), which harnesses the computational power of deep learning using convolutional neural network architectures applied to surface meshes of geometric surfaces of the brain. This method provides a data-driven approach to determine an individual’s “brain age” using T1 weighted brain MRI data. Our team is the first group to apply geometric deep learning to image-based applications and this approach has proven successful in the accurate prediction of brain age. We have previously estimated brain age with SBDL in a large sample of healthy participants ranging in age from 6-86 years, achieving state-of-the-art performance (average error less than 4.5 years with a coefficient of correlation of 0.95 when compared to chronologic age). Additional investigations in individuals with dementia as well in Super Agers (individuals who demonstrate relative preservation of cognitive functioning well into advanced years), have shown that “brain age” more accurately reflects cognitive and functional status than chronologic age (manuscrip
StatusFinished
Effective start/end date5/1/214/30/23

Funding

  • University of Washington (UWSC12801 // 5R33AG067069-02)
  • National Institute on Aging (UWSC12801 // 5R33AG067069-02)

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