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Courses offered

Computational Neuroscience

Academic Lead: Prof Robert Leech and Dr Pete Hellyer

Date: 1st July 2019 – 5th July 2019


Venue: Seminar 1 and 2, Main Building. Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London. View Map.


  • External Early bird: £675 (till 23/05/19, price thereafter £750)
  • KCL Staff Early bird: £506.25 (till 23/05/19, price thereafter £562.5)
  • KCL Student Early bird: £337.5 (till 23/05/19, price thereafter £475)
  • Other Student Early bird: £506.25 (till 23/05/19, price thereafter £562.5)
  • Kings Health Partner Early bird: £506.25 (till 23/05/19, price thereafter £562.5)

(That is, 50% discount to King's College London PhD students, 25% discount to other students and staff at King's College London and King's Health Partners)

Last booking: 20th June 2019

BOOKING: For more information and to book visit this link



This course involves the application of statistical and modelling approaches - acquired in earlier BHI courses - to brain imaging; this will involve working with large structural and functional neuroimaging datasets to develop brain biomarkers of neurological and psychiatric disorders. The course aims to introduce core themes and techniques in neuroimaging and computational modelling in neuroscience, using Python and other relevant programming languages. To relate statistical models and methods (including knowledge from other modules) to discover biomarkers and stratify patients with neurological and psychiatric disorders.


The module will assume that participants are familiar with statistical modelling approaches as can be gained from the BHI Introduction to Statistical Modelling course run in January, the BHI Multilevel and longitudinal Modelling run in February, the BHI Structural Equation Modelling in April and the BHI Causal Modelling and Evaluation course in early July.

Some experience of syntax-based statistical software such as R, would be helpful (such as can be obtained from the BHI Introduction to Programming course running in October. Participants will need to bring their own laptop computer with Python installed.


Subject specific: Knowledge, Understanding and Skills

On successful completion of this module the participant should be able to

  • Use Python and other high-level programming languages to manipulate and fit statistical models to large 3D neuroimaging datasets involving up to 1000s of patients and controls.
  • Understand basic principles of data processing (particularly pre-processing) involved in different types of neuroimaging
  • Formulate appropriate data analysis approaches to specific neuroscience questions, including neurological and psychiatric disorders and relating neural measures with measures of clinical outcome.
  • Apply a range of statistical approaches to neuroimaging datasets, both functional and structural neuroimaging: including general linear models, sparse models, predictive models, data reduction approaches, and causal models.
  • Understand basics of computational neural simulations and how can be related to neurological and psychiatric disorders

General: Knowledge, Understanding and Skills

On successful completion of this module the participant should be able to

  • Have practical experience in developing biomarkers from neuroimaging datasets relevant to clinical, engineering, pharmaceutical and machine learning situations
  • Experience in applying a range of data analysis and modelling approaches from to practical problems with real-life applications
  • Ability to work with big datasets
  • Communicate ideas related to neuroimaging and neuroscience effectively by particularly focusing on visual presentation of 3- and 4-D neuroimaging results.
  • Select and interact with large publicly available neuroimaging datasets
  • Interact effectively in a group


Please note the following:

If you would like to pay by internal transfer, please contact 

Your place will not be confirmed until payment has been made.

Failure cancel without sufficient notice will forfeit your course fee and access to future courses.

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