Designing novel imaging probes and methods for clinical fluorine-19 MRI cell tracking
10-11 am Friday 30th of June 2017, IoPPN boardroom
10-11 am Friday 30th of June 2017, IoPPN boardroom
10-11 am Friday 2nd of June 2017, IoPPN boardroom
This session is a look at how genetic risk and protection for late-onset Alzheimer’s disease influence brain function in young adults. With a focus on multi-modal MRI techniques, I will examine how brain structure, functional activity, and cerebrovascular health vary across the lifespan in carriers of the APOE gene variants.
10-11 am Wednesday 31st of May 2017, Robin Murray B, main IoPPN building
To develop an image analysis technique that distinguishes pseudoprogression from true progression by analyzing tumour heterogeneity in T2-weighted images using topological descriptors of image heterogeneity called Minkowski functionals (MFs).
Using a retrospective patient cohort (n = 50), and blinded to treatment response outcome, unsupervised feature estimation was performed to investigate MFs for the presence of outliers, potential confounders, and sensitivity to treatment response. The progression and pseudoprogression groups were then unblinded and supervised feature selection was performed using MFs, size and signal intensity features. A support vector machine model was obtained and evaluated using a prospective test cohort.
The model gave a classification accuracy, using a combination of MFs and size features, of more than 85% in both retrospective and prospective datasets. A different feature selection method (Random Forest) and classifier (Lasso) gave the same results. Although not apparent to the reporting radiologist, the T2-weighted hyperintensity phenotype of those patients with progression was heterogeneous, large and frond-like when compared to those with pseudoprogression.
Analysis of heterogeneity, in T2-weighted MR images, which are acquired routinely in the clinic, has the potential to detect an earlier treatment response allowing an early change in treatment strategy. Prospective validation of this technique in larger datasets is required.
2:30-3:30pm Thursday 4th of May 2017, CNS 3.11A and B
Prediction and predictive codes are now ubiquitous computational viewpoints from which we may better understand neural circuit organization and signal transmission in the brain.
In this talk I will present a predictive view of changing brains, over lifespans, based on the Free Energy Principle, a theory of hierarchical empirical Bayesian inference in the brain (Friston 2013). This particular formulation of the Bayesian brain produces predictive coding schemes that have been used to inform the principles of perception, action and decision-making, accounting for how sensory information combines with our own prior beliefs about the world to shape brain activity and behavior. There are many ways that a brain could perform Bayesian inference and the hypothesized scheme under the Free Energy Principle in the perceptual domain posits a variational algorithm where posterior density estimation is recast as an optimization problem. In this guise the scheme becomes a predictive coding algorithm, with hierarchical structure and attribution of optimization dynamics to particular components of neuronal circuits.
In this talk I will present evidence from neuroimaging studies of brain circuits (using dynamic causal models) that age-related connectivity changes are commensurate with long-term Free-Energy minimization. I will present work from sensory learning, memory and decision making paradigms that show that the neurobiological implementations of prior beliefs grow stronger in older brains. I will explore how this relates to faster timescales of prediction in terms of electrophysiological correlates.
10:00-11:00 Friday 7th April 2017, IoPPN boardroom, Main Building
10:00-11:00 Friday 3rd February 2017, IoPPN boardroom, Main Building
Different measures of cortical morphology have been shown to index distinct aspects of inter-individual differences (e.g., volume, thickness, surface area, gyrification). Here I consider the additional measure of structural complexity, as quantified by fractal dimensionality. Using several open-access MRI datasets, providing a combined sample of over 1000 adults across the adult lifespan, I examined the relationship between each measure and age-related differences in brain morphology. In a separate set of analyses I further examined the test-retest reliability of each measure. When examining the relationship between brain morphology and inter-individual differences, it is important to consider the most appropriate measure. For instance, it has been established that age-related differences are reflected most in cortical thickness, rather than surface area or volume. Here I demonstrate that fractal dimensionality, which incorporates shape-related properties, to be a more sensitive measure of age-related differences in cortical and subcortical structures. Limitations of this fractal dimensionality measure will also be discussed.
14:00-15:00 Tuesday 22nd November 2016, Seminar room 5, Main Building
I will present data from both a hypothesis-driven approach as well as from transcriptomic profiling of developing Purkinje cells that link specific autism genes to the developing cerebellum. I will also present a new method of differentiating cerebellar neurons from human induced pluripotent stem cells (iPSCs) that our group has established.
The Becker group is interested in elucidating the genetic and molecular mechanisms that underlie cerebellar development and that how impairment of these processes results in cerebellar diseases including autism spectrum disorder.