Bio

Sarah Morgan is a Research Fellow at Cambridge University. She completed her PhD at the Cambridge Physics department, in the Theory of Condensed Matter group (TCM). She then moved to the Brain Mapping Unit in the Cambridge Psychiatry department to begin her postdoctoral work. In 2017, Sarah won a Henslow Research Fellowship at Lucy Cavendish College, Cambridge. In 2019, she was awarded a Turing Fellowship to investigate whether speech can be used to detect psychotic disorders.

Sarah is a co-organiser of the Cambridge Networks Network, which brings together researchers from in and around Cambridge with an interest in complex networks (http://www.cnn.group.cam.ac.uk/). She is also passionate about making STEM more diverse, and co-founded the Cambridge group for women and non-binary people in physics (https://www.cavendishinspiringwomxn.co.uk/).

Research interests

Sarah's research applies data science approaches- including network science, machine learning and bioinformatics- to better understand and predict mental health.

A main research focus is using MRI brain imaging to study schizophrenia and other mental health disorders. In particular, MRI brain images can be used to investigate brain connectivity, by calculating MRI brain networks where nodes represent large scale brain regions and edges represent connectivity between brain regions. MRI brain networks from patients with schizophrenia often show altered connectivity patterns compared to healthy volunteers. Sarah's research explores both whether we can use these connectivity patterns to predict individual patients' disease trajectories, and what they can teach us about the biological mechanisms underlying schizophrenia.

Sarah is also interested in using data science to investigate other aspects of mental health, for example using network science and natural language processing methods to study patients' speech. In schizophrenia in particular, patients often exhibit altered speech patterns, but to date there are no quantitative measures of altered speech available to clinicians. Recent work has also suggested that patients' speech might be able to predict their disease outcomes. Data science approaches therefore offer an exciting opportunity to provide quantitative speech markers which could revolutionise healthcare of this debilitating disorder.