The Neuroscience Department is a basic research department with a diverse portfolio of research programs and approaches ideally suited for the 21st century challenge of understanding normal and diseased brain function. The Department provides a collaborative environment in which multidisciplinary and multiscale approaches provide fertile ground for identifying new mechanisms of neurological disorders. Our expertise spans scales from genetic, molecular (atomic), cellular and circuit levels to the whole organism behavioral and clinical levels. To achieve this multi-scale approach, members of the department have backgrounds from many disciplines: biology (genetics, molecular and cellular), physics, statistics, computer science and medicine.
Specific research topics span multiple brain regions, allowing studies of the neural basis of sensory perception, motivation and reward, decision-making, learning and memory. The principal clinical applications encompass neurological, psychiatric and neurodegenerative disorders: with projects specifically addressing autism spectrum disorders, learning disabilities, schizophrenia, mood disorders, Alzheimer’s disease, age-related cognitive decline, addiction and movement disorders. We believe that such an integrated basic research approach along with clinical collaborations, and in some cases with embedded clinicians, provides an essential environment for tackling diseases of the most complex organ of the human body, the brain.
- We aim to understand the molecular, cellular, and network mechanisms that shape dynamic connectivity within the brain and result in learning, memory, sensory perception, social communication and cognition.
- We aim to understand how genetic, epigenetic and environmental factors individually and collectively shape dynamic brain connectivity, and result in neurological/psychiatric disorders.
- We aim to develop pharmacological and genetic tools for preventing/alleviating/curing peripheral and central neuronal circuit deficits.
- We aim to leverage computational approaches for analysing and modelling brain connectivity.