Supervisor: Dr. Christopher Madan
School: Psychology

Description:

Standard measures of brain structure such as cortical thickness and subcortical volume are woefully inadequate in characterising age-related differences in brain structure. However, the development of novel measures that measure structural complexity (e.g., fractal dimensionality), sulcal morphology, and topological spatial frequency are providing new insights into how brain structure differs between individuals (e.g., between patients and healthy controls) and over time. Further developments will provide novel insights into the biological factors that influence brain morphology and how we can better quantify inter-individual differences in structure as a biomarker.

Required skills:

• Demonstrable programming ability in Matlab or Python

Desirable skills:

• Previous experience with structural MRI data in FreeSurfer

Background readings:

1. Madan, C. R., & Kensinger, E. A. (2016). Cortical complexity as a measure of age-related brain atrophy. NeuroImage, 134, 617-629.
2. Madan, C. R. (2017). Advances in studying brain morphology: The benefits of open-access data. Frontiers in Human Neuroscience, 11, 405.
3. Madan, C. R., & Kensinger, E. A. (2018). Predicting age from cortical structure across the lifespan. European Journal of Neuroscience, 47, 399-416.
4. Madan, C. R. (in press). Shape-related characteristics of age-related differences in subcortical structures. Aging & Mental Health.

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