The impact of schizophrenia-associated copy number variants on cortical network dynamics

Schizophrenia (SZ) is a disabling mental health disorder that affects approximately 1% of the UK population. The condition typically has its onset in adolescence or early adulthood and is associated with changes in how reality is perceived (reality distortion) and difficulties in thinking. SZ has significant long-term impacts on the sufferer and their family. Unfortunately, over the past decades we have made little progress in improving the treatment of this disorder. This is because we lack adequate understanding of the biological causes of schizophrenia.

Recent major advances in research into the genetic basis of schizophrenia now offer hope of better understanding of the biological basis of SZ, potentially opening up new avenues for early diagnosis and treatment. One of the most striking genetic findings has been that changes in chromosomal structure, referred to as Copy Number Variants (CNVs), can significantly increase risk for schizophrenia. Studying people with these schizophrenia-associated CNVs (SZ-CNVs) therefore offers a new route to understanding the brain changes associated with risk for the disorder.

In the current proposal we aim to investigate how some of the most common SZ-CNVs affect brain function. We hypothesise that these different SZ-CNVs have common effects on brain function by altering the regulation of specific types of neurons in the brain cortex. We further hypothesise that this affects the way different neurons communicate with each, contributing to the reality distortion and thinking difficulties seen in the disorder.

The human brain is difficult to investigate directly because of its inaccessibility. We will therefore combine different methods to investigate the impact of SZ-CNVs on the brain. Firstly, we will use human brain imaging to determine changes in brain activity and connectivity in young people carrying SZ-CNVs. Secondly, we will study mice carrying similar chromosomal changes to allow us to investigate in more depth than is possible in humans. Finally, we will study the changes in firing and connectivity in human neurons derived from patients with SZ-CNVs.

These different methods will enable us to build up a more complete picture of the way these SZ-CNVs affect the brain than any single technique alone could. However, it remains a challenge to compare and integrate data across these methods. To allow us to do this we will use computer modelling to combine and integrate our findings. We will subsequently investigate the ability of these computer models to predict cognitive difficulties and psychiatric symptoms in people with SZ-CNVs, including the early symptoms of schizophrenia. The refinement and testing of these models will represent a means of improving our understanding of the pathological effects of SZ-CNVs.

Overall, this work will enable us to advance understanding of how SZ-CNVs impact on brain function and connectivity. The insights we will gain will represent an important step towards improving understanding of the biological causes of schizophrenia, with the aim of enhancing the early diagnosis and treatment of the condition.

The past decade has seen exceptional progress in the identification of genetic risk factors for schizophrenia (SZ), enabling a 'genomics first' approach to psychiatry. One of the most robust genetic findings is that copy number variants (CNVs), involving the deletion or duplication of large (>1 kilobase) sections of DNA, are strongly associated with risk for SZ. These schizophrenia-associated CNVs (SZ-CNVs) offer a powerful route to understanding the neurobiological mechanisms connecting genetics to psychiatric risk. We have shown that SZ-CNVs are enriched for genes involved in synapses, especially the NMDA receptor complex, post-synaptic density and ARC complex. Furthermore, we have shown that diverse SZ-CNVs produce convergent impacts on a range of neurocognitive and psychiatric symptoms.

In this proposal we will examine the neural basis of this convergence across SZ-CNVs. We will use magnetoencephalography (MEG) to investigate the impacts of CNVs on cortical activity and functional connectivity in human SZ-CNV carriers, building on our pilot data. We will in parallel study mice bearing syntenic CNVs enabling us to investigate the cellular and circuit-level causes of these MEG connectivity changes using EEG, local field potential recordings and linear electrode arrays. Finally, we will study the functional connectivity of human neurons derived from carriers of SZ-CNVs using induced pluripotent stem cell technology.

To integrate data across these different methods we will apply computational modelling to develop a model of the impact of SZ-CNVs on cortical circuits. These models will be evaluated pharmacologically and by investigating their ability to predict psychiatric symptoms and neurocognitive impairments in SZ-CNV carriers. The insights we will gain will provide a better understanding of the pathological effects of CNVs and support genomically-informed approaches to the diagnosis and treatment of psychiatric disorders.