Integrating circadian, neuroimaging and genetic data to investigate major depression and bipolar disorder
Lead Research Organisation:
University of Glasgow
Department Name: College of Medical, Veterinary, Life Sci
Abstract
Studentship strategic priority area:Basic and Clinical Research
Keywords: Circadian rhythms, depression, bipolar disorder, genetics, brain imaging.
Disruption to daily circadian rhythms is associated with greater risk of mood disorder and impaired cognitive ability. However, so far most studies have used subjective measures of rhythmicity, small or cross-sectional samples, and haven't examined potential mediators and moderators of effects. This interdisciplinary project will use chronobiological, neuroimaging and genetic data from the landmark UK Biobank cohort. The cohort includes lifestyle, sociodemographic and genetic data for over 500,000 individuals and brain MRI for over 30,000; and our group has already derived objective accelerometry-based measures of circadian rest-activity rhythms for 100,000 individuals. Using this wealth of data, machine learning and regression methods can help determine which objective rhythmicity variables best predict mood disorder and related outcomes; and whether the influence of circadian disruption on mood and cognition is partly due to effects on brain structure, or is moderated by genetic factors. Using data from linked health records of UK Biobank participants, models will be developed to assess whether mood disorder episodes can be predicted from a combination of circadian rhythmicity, neuroimaging, genetic, sociodemographic and lifestyle factors. This project will provide training in a wide range of research skills relevant to precision medicine, including epidemiology, genomics, neuroimaging and machine learning.
Keywords: Circadian rhythms, depression, bipolar disorder, genetics, brain imaging.
Disruption to daily circadian rhythms is associated with greater risk of mood disorder and impaired cognitive ability. However, so far most studies have used subjective measures of rhythmicity, small or cross-sectional samples, and haven't examined potential mediators and moderators of effects. This interdisciplinary project will use chronobiological, neuroimaging and genetic data from the landmark UK Biobank cohort. The cohort includes lifestyle, sociodemographic and genetic data for over 500,000 individuals and brain MRI for over 30,000; and our group has already derived objective accelerometry-based measures of circadian rest-activity rhythms for 100,000 individuals. Using this wealth of data, machine learning and regression methods can help determine which objective rhythmicity variables best predict mood disorder and related outcomes; and whether the influence of circadian disruption on mood and cognition is partly due to effects on brain structure, or is moderated by genetic factors. Using data from linked health records of UK Biobank participants, models will be developed to assess whether mood disorder episodes can be predicted from a combination of circadian rhythmicity, neuroimaging, genetic, sociodemographic and lifestyle factors. This project will provide training in a wide range of research skills relevant to precision medicine, including epidemiology, genomics, neuroimaging and machine learning.
Organisations
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| MR/N013166/1 | 01/10/2016 | 30/09/2025 | |||
| 2284218 | Studentship | MR/N013166/1 | 09/09/2019 | 08/03/2023 | Natasha Sangha |