Project 2: Detection of Regional Biomarkers of Brain Ageing using Magnetic Resonance Imaging

Global brain age prediction from MR images and its comparison with chronological age has proven to be a reliable biomarker for brain disorders, like Parkinson’s and Alzheimer’s disease, but also other conditions like Down’s Syndrome and HIV have been linked to the exacerbation of the brain ageing process.

The brain ageing models first proposed used handcrafted features, such as volumes of cortical structures and image texture, to develop a regression model to estimate brain age. With the success and rapid growth of deep learning, brain age prediction models shifted towards using convolutional neural networks (CNNs) for the brain age prediction task. The advantages of CNNs are that they can learn the features directly from the data (i.e., no need to handcraft features), and these deep learning models often produce more accurate predictions than traditional methods. Several deep learning models report an average brain age prediction error < 2 years.

Age-related brain changes are characterized by region-specific and nonlinear patterns of processes, such as cell growth and synaptic pruning, and widespread brain atrophy that happens during brain ageing. Although accurate deep-learning-based global brain age prediction models can indicate signs of accelerated brain ageing and brain disorders, they lack the spatial specificity to highlight which regions of the brain are the most affected.

The concept of regional brain age prediction based on MR image features is new, and it overcomes the limitations of having a single global index. The regional brain age prediction methodology proposed and investigated in this study is novel and will create an advanced paradigm for spatially resolving brain ageing mechanisms based on imaging features. This new paradigm will expand the field to new exciting directions that will allow us to understand better the normal brain ageing mechanisms and how different disorders affect the human brain.

I anticipate that regional brain age predictions will be a superior biomarker compared to global brain age prediction across many disorders. The ability to detect regions of the brain that show signs of accelerated ageing will allow us to better understand the mechanism of disease, and to develop and verify the efficacy of new therapies and interventions that can counter the effects of accelerated ageing.