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Data Science approaches for the analysis and monitoring of atypical brain states

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

About This PhD Project

Project Description


In many neurological disorders, comparable initial neurological deficits may result in a wide range of functional and behavioural deficits in the long term, as the condition develops. However, the prediction of functional and behavioural outcomes is a requirement for the selection and implementation of a specific intervention approach. Data-driven predictive models can help to investigate the heterogeneity within a given class of neurological disorders and guide the identification of homogeneous subgroups of patients. Feature selection, regression, classification and clustering algorithms will be combined to provide innovative solutions to diagnosis and monitoring.

References

E.E. Bron et al., "Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge", NeuroImage, an Elsevier Journal of Brain Function, 1 May 2015, 111:562-79.
A. Sarica, G. Di Fatta, G. Smith, M. Cannataro, D. Saddy, "Advanced Feature Selection in Multinominal Dementia Classification from Structural MRI Data", in the Proceedings of the Workshop on Computer-Aided Diagnosis of Dementia based on structural MRI data (CADDementia), Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Sept. 18, 2014, Boston, MA, USA.

Related Subjects

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