Shaping behavioural flexibility; insights from imaging-genetics and mathematics

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• Jeffrey Glennon (University College Dublin)
• Friday 23 June 2023, 11:30-12:30
• Seminar Room 1, Newton Institute.

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RNTW04 - Synergistic workshop on Rich and Nonlinear tomography aimed at drawing together all strands of both methods and applications with new insights

Behavioural processes are key to an organism’s response to the environment and to each other. These behavioural processes are often conditioned either by reward or punishment and involve changes to learning, memory, attention and behavioural flexibility. Behavioural flexibility, or the adaptive change of behaviour in response to changing environmental contingencies, is altered in disorders that emerge in early life such as autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), Tourette’s Syndrome, callous-unemotional traits / psychopathy; those that emerge in adolescence, including schizophrenia and mood disorders; and those in later life including dementias. While many of these conditions share flexibility deficits, the treatment of flexibility as a discrete feature in its own right is a challenge, owing to the heterogeneous nature, severity and patterns of co-morbid symptoms. Behavioural changes can be identified using tasks such as reversal learning, and attention set shifting. The CANTAB (Cambridge Neurophysiological Test Automated Battery) test battery, a standard in cognitive research can also assess behavioural flexibility using tasks incorporating discrimination reversal (switching reward-response associations) and extra-intra dimensional set shifting (attention and cognitive flexibility). These document changes not only in rule making but also rule breaking in autism, obessive compulsive disorder and psychopathy. Recent research has demonstrated that behavioural flexibillity is under the control of insulin signalling. Whle the brain circuitry underlying different aspects of behavoural inflexibility has been well documented in magnetic resonance imaging (MRI) datasets, we are on the cusp of linking genomics,  proteomics and metabolomics datasets to (i) both brain and somatic phenotypes and (ii) this structural and functional MRI data in both clinical and preclinical datasets. The development of new mathematical tools has been instrumental towards enhancing our understanding here.

This talk is part of the Isaac Newton Institute Seminar Series series.

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