Project ID NS-MH2023_23


Co Supervisor 1A IoPPN/PsychologyWebsite

Co Supervisor 1B IoPPN/Biostatistics and Health InformaticsWebsite

Characterising structural brain development trajectories between childhood and adulthood and their relationship to mental health outcomes

Characterizing brain development is a fundamental task in neuroscience. Yet, most developmental neuroimaging studies to-date have relied on cross-sectional data, which precludes investigation of developmental changes and has hampered progress in understanding the mind-brain relationship during development.

In collaboration with the Danish Centre for Magnetic Resonance (DCMR), the PhD project will close this gap in our understanding of brain development. Leveraging longitudinal modelling techniques and emerging cohorts like HUBU (N = 90, 12-waves, ages 7 -21) and ABCD (N = 11,000, currently 3 waves, ages 9 -14), the PhD student will develop new approaches for capturing longitudinal changes in brain structure and link these to biological (e.g., puberty) and behavioural predictors (e.g., alcohol usage) and lifespan outcomes (e.g., mental health). For example, the PhD student may use nonlinear mixed models to investigate whether alcohol usage in HUBU predicts changes in cortical thickness and whether the age of alcohol exposure moderates these effects, providing insights into sensitive periods.

This PhD is expected to drive theory and methods development in the field and inform prevention and intervention work.

Year 1: Training in statistical modelling (e.g., nonlinear models), coding (e.g., in R), psychometrics and longitudinal design; Systematic literature review; Study design
Years 2/3: DCMR secondment: training in neuroimaging (e.g., DTI and structural MRI acquisition and analysis); Data analysis
Years 3/4: Writing up studies for scientific papers and thesis

One representative publication from each co-supervisor:

Fuhrmann, D., Skak Madsen, K., Baruel Johansen, L., Baaré, W. F. C., Kievit, R. A. (preprint). The midpoint of cortical thinning between late childhood and early adulthood differs across individuals and regions: Evidence from longitudinal modelling in a 12-wave sample., preprint doi: 10.1101/2022.02.10.479868

Nguyen H, Moreno-Agostino D, Chua KC, Vitoratou S, Prina AM (2021) Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts. PLOS ONE 16(4): e0248844.