The molecular basis of cognitive flexibility
Application deadline: 23 November 2018
Start date: October 2019
Research theme: Neuroscience and Mental Health
Cognitive flexibility is a key aspect of learning and refers to the ability to alter responding in reaction to a changing environment. Deficits in cognitive flexibility are associated with disorders such as schizophrenia, autism and OCD. Although the neural circuitry is understood well (involving the cortico-striatal regions) the molecular basis of cognitive flexibility remains understudied.
Change in the expression of many genes is critical for synaptic plasticity, but to date there has been no systematic analysis of changes in gene expression during the course of cognitive flexibility.
Project aims and method
This project will address this by asking: What are the gene expression changes that underpin cognitive flexibility, and are they enriched for genes that contribute to schizophrenia and/or autism?
We can measure cognitive flexibility using reversal learning paradigms that assess the attentional and inhibitory processes needed to switch efficiently from a pre-potent correct response to a previously incorrect response. Subjects learn to associate a reward with a correct response and avoid an incorrect one. Once acquisition is reached (i.e. peak correct choice), the contingencies are reversed, meaning the incorrect response is now correct, and the correct response is now incorrect. Immediately following reversal, a subject’s score is close to 0% correct, but over the course of reversal learning behaviour adapts as the previous correct responding is inhibited, and previously incorrect responding is disinhibited. Then new learning occurs and correct responding eventually reaches acquisition levels again (re-acquisition).
RNA-seq data will be obtained from rats in key brain regions (nucleus accumbens, medial dorsal striatum, medial prefrontal cortex, orbitofrontal cortex) at critical periods of reversal learning:
- At acquisition (>85% correct);
- After the first reversal session (~0% correct);
- At chance responding (50% correct);
- At re-acquisition (>85% correct).
This will generate a differentially expressed gene set (DEG-set) of which we can ask questions about changes in gene expression between:
- acquisition and first reversal;
- first reversal and 50% responding;
- first acquisition and re-acquisition.
The DEG-set will also be explored using over-representation analysis of gene functional annotations (e.g. GO ontology terms) to see whether expression changes converge on specific biological pathways. Gene co-expression network strategies such as Weighted Gene Co-expression Network Analysis (WGCNA), will be applied to detect any relationship between sets of genes and gene regulatory networks in the cortico-striatal systems.
Finally, you will further interrogate their data by examining the link between reversal learning, and schizophrenia and autism. Using the large genomic datasets available within the MRC CNGG they will examine whether the reversal learning DEG-sets are enriched for genetic risk factors contributing to schizophrenia and autism.
Dr Araxi Urrutia, University of Bath.