Despite interindividual differences in cortical and subcortical structure, cross‐sectional and longitudinal studies have demonstrated a large degree of population‐level consistency in age‐related differences in brain morphology. Regional brain cortical thickness or subcortical volume has been shown to be a sensitive marker of function, recent work from my laboratory has shown that measures of shape can be more sensitive to inter-individual differences in structure (Madan & Kensinger, 2016, NeuroImage; Madan & Kensinger, 2017, Neurobiology of Aging). In my prior work, shape showed stronger age-related effects than volume, and demonstrated higher test-retest reliability (Madan & Kensinger, 2017, Brain Informatics) and resilience to motion-related artifacts (Madan, 2018, PeerJ).
These shape measures are important because, if a structure is larger or smaller between individuals after adjusting for overall brain volume, then this must have some influence on tissue adjacent to the structure of interest, and therefore the underlying shape (e.g., Madan, 2018, Aging & Mental Health).
Volumetric measurements do not capture these differences in shape, and characterizing the shape of a structure may provide additional sensitivity to understanding the neurobiology. Moreover, while much of the existing work in this area has been examined in humans, it is unclear how general these principles of brain structure are, and if they generalize to other species,namely non-human primates.
In this project, we will examine the fundamental principles of brain structure organization and the relationship between structure and cognition. Specific questions will be determined by the student’s interests. A few example questions include:
- How does cortical and subcortical brain structure change in healthy aging and with dementia (in humans)?
- How do age-related differences in brain structure compare between humans and non-human primates?
- How do structural covariance and connectivity (i.e., diffusion tensor imaging) provide unique information regarding brain structure organization?
- How do different brain structure metrics and topography relate to cognitive functions?
Research in this project will primarily involve processing structural brain MRI data to determine summary data that can be then compared across individuals and brain regions. This will include using innovative techniques to segment and parcellate the structural MRIs to discrete regions, which we can then characterize using measures such as thickness, volume, and gyrification, as well as novel shape-measures such as fractal dimensionality and spherical harmonics. These structural measures will then be related to age and cognitive measures using machinelearning techniques. Recent papers may provide useful examples of this approach (Madan, 2018, Aging & Mental Health; Madan & Kensinger, 2018, European Journal of Neuroscience).
This project will use pre-existing, open-access datasets such as the Human Connectome Project (HCP) and Cambridge Centre for Ageing and Neuroscience (CamCAN) datasets—also see Madan (2017, Frontiers in Human Neuroscience) for an overview of many of the available datasets. Non-human primate data will also come from existing datasets that are publicly available. New human MRI data may be acquired, based on the specific research direction taken by the student.