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Gaining insight into the biology of learning through Phelan McDermid Syndrome

Principal Supervisor: Dr Jennifer Cook, School of Psychology

Co-supervisor: Dr. Eduardo Alonso, Head of Department, School of Mathematics, Computer Science & Engineering, City University London

PhD project title: Gaining insight into the biology of learning through Phelan McDermid Syndrome

University of Registration: University of Birmingham


Project outline:

Aims:

A recent breakthrough using isolated human Phelan McDermid Syndrome neurons in the laboratory has revealed the pathway from genes to neuronal dynamics1. The pathway from neuronal dynamics to learning, however, remains poorly understood. This project will develop and test a computational model of learning in Phelan McDermid Syndrome which will provide insight into the gene-neuron-learning pathway in this syndrome and ultimately help us understand the biology of learning.

Backgr­ound:

Understanding how the nervous system gives rise to our powerful ability to rapidly learn from experience has been a major challenge for psychologists and neuroscientists for decades. Though various neurochemicals have been associated with the process of learning2–4, we are a long way from a comprehensive understanding of the gene-neuron-learning pathway. Recent studies have linked mutations in the SHANK 3 gene to learning difficulties across multiple clinical conditions5–7. However, little is known of the functional role of SHANK3 in the learning process.

Phelan-McDermid syndrome (PMS) is a lifelong rare genetic syndrome characterised by learning disabilities8–10, which can mean that individuals with this syndrome never learn to speak and require specialist schooling. PMS, which affects approximately 6,500 individuals in the UK alone, is caused by a micro-deletion in the SHANK3 gene11. PMS therefore offers a unique opportunity to elucidate the SHANK3 gene-neuron-learning pathway and thereby enhance our understanding of the biology of learning.

At present, little is known about the role of SHANK3 in the biology of learning. Laboratory studies using mouse models of PMS and human neuronal cultures1 have demonstrated that SHANK3 mutations lead to increased neuronal resistance and decreased dendritic arborisation. In other words, neuronal circuits with SHANK3 mutations are analogous to electrical circuits with sparse connections between nodes, and where the nodes act like resistors, inhibiting the amount of current that can flow through the circuit. In parallel work, both human and mouse PMS studies have demonstrated abnormalities in various tests of learning ability5,13–15. However, if we are to understand the link between genes, neuronal dynamics and learning, we need to know how abnormal neuronal dynamics relate to the learning process in humans with PMS.

Computational models have been a key tool in linking behavioural correlates of learning to underlying biological mechanisms16–20. A class of computational model called neural network (NN) models simulate the dynamics of the brain by treating neurons as nodes in a network, and axons as connections between nodes. NN models are particularly well suited to modelling conditions, such as PMS, where laboratory studies have provided rich information about neuronal dynamics and architecture. At present, however, a computational model of learning in PMS has never been developed. Our project will fill this gap, bringing cutting-edge computational modelling techniques to bear on this important issue and providing valuable insights into the pathways underlying the biology of learning.

Methods:

Dr Cook and Dr Alonso have previously developed touch-screen learning tasks, based on animal studies of reinforcement learning, that are appropriate for individuals with intellectual disabilities. With the PhD student, we will use this battery to collect data from typically developing (TD) individuals and from individuals with PMS. Using this data, we will develop computational models which simulate responses of TD and PMS participants on the task battery. Comparing these models will inform us about the way in which the learning process differs as a function of SHANK3 mutation and will thus help to elucidate the role of SHANK3 in the biology of learning.

References:

  • Yi, F. et al. Autism-associated SHANK3 haploinsufficiency causes Ih channelopathy in human neurons. Science 352, aaf2669 (2016).
  •  Niv, Y. Reinforcement learning in the brain. J. Math. Psychol. 53, 139–154 (2009).
  •  Schultz, W. & Dickinson, A. Neuronal coding of prediction errors. Annu. Rev. Neurosci. 23, 473–500 (2000).
  •  Waelti, P., Dickinson, A. & Schultz, W. Dopamine responses comply with basic assumptions of formal learning theory. Nature 412, 43–48 (2001).
  •  Durand, C. M. et al. Mutations in the gene encoding the synaptic scaffolding protein SHANK3 are associated with autism spectrum disorders. Nat. Genet. 39, 25–27 (2007).
  •  Gauthier, J. et al. De novo mutations in the gene encoding the synaptic scaffolding protein SHANK3 in patients ascertained for schizophrenia. Proc. Natl. Acad. Sci. U. S. A. 107, 7863–7868 (2010).
  •  Nemirovsky, S. I. et al. Whole genome sequencing reveals a de novo SHANK3 mutation in familial autism spectrum disorder. PloS One 10, e0116358 (2015).
  •  Copping, N. A. et al. Touchscreen learning deficits and normal social approach behavior in the Shank3B model of Phelan-McDermid Syndrome and autism. Neuroscience (2016).
  •  Sarasua, S. M. et al. Clinical and genomic evaluation of 201 patients with Phelan-McDermid syndrome. Hum. Genet. 133, 847–859 (2014).
  •  Sarasua, S. et al. 22q13.2q13.32 genomic regions associated with severity of speech delay, developmental delay, and physical features in Phelan-McDermid syndrome. Genet. Med. 16, 318–328 (2014).
  •  Phelan, K. & McDermid, H. E. The 22q13.3 deletion syndrome (Phelan-McDermid syndrome). Mol. Syndromol. 2, 186–201 (2012).

 

BBSRC Strategic Research Priority: Molecules, Cells and Systems

Techniques that will be undertaken during the project:

  • Refinement of in-house behavioural tests (with Dr Cook)

  • Computational modelling of reaction times and choice behaviour (with Dr Alonso)


Contact: Dr Jennifer Cook, School of Psychology