Skip to Content »

 Martin McKeown

Download Seminar Poster PDF

Download PresentationPDF

Talk Title:
What can computational approaches teach us about Parkinson’s Disease ?

December 11, 2008, 6:00pm

University of British Columbia, Department of Electrical and Computer Engineering, Biomedical Signal and Image Computing Laboratory

Martin McKeown

Parkinson’s disease (PD) is the second most common neurodegenerative
disease in Canada, after Alzheimer’s disease. Although treatments (both
medical and surgical) are available for PD, and can have dramatic
beneficial effects especially early in the disease, they treat the
symptoms of the disease without altering the overall progression. Using
fMRI, we can non-invasively probe the normal and parkinsonian brain, but
the data require extensive processing to get meaningful results. We will
discuss the roles of Independent Component Analysis (ICA), Dynamic
Bayesian Networks (DBNs), Probabilistic Boolean Networks (PBNs), Large
Deformation Diffeomorphic Metric Mapping (LDDMM), replicator dynamics,
as well as 3D moment invariants in the analysis of these data sets.
Additionally, we will describe how second order linear dynamical system
theory can be applied to manual tracking data from PD and normal
subjects. Finally, since recent research has demonstrated that
functionally, the Parkinsonian state is characterized by the emergence
of pathological oscillations in the beta range (12-30 Hz) within basal
ganglia / cortical loops, we will describe how frequency-domain
analysis, such as partial directed coherence (PDC) can be used to
investigate the electroencephalogram (EEG) recordings from PD subjects.
These technologies will be put in the context of exploring compensatory
mechanisms in PD, capable of ameliorating overall disability.

Introductory Speaker:

Cydney Nielsen

Talk Title:
Genomic Data Visualization: Making Sense of Large-Scale Data Sets

Download PresentationPDF

Jones Laboratory, Genome Sciences Centre