Back to projects
Understanding mental health with data science
Project Overview
Led by Dr Sarah Morgan, this group applies data science approaches to better understand mental health conditions, including machine learning, network science and NLP methods.
A core area of focus is the use of MRI to study brain connectivity in schizophrenia and other mental health conditions. The group uses brain MRI to estimate brain networks, where nodes represent macroscopic brain regions and edges represent connectivity between regions. This allows exploration of whether connectivity patterns can be used to predict individual patients’ disease trajectories and what such patterns reveal about the biological mechanisms underlying mental health conditions, for example by relating brain MRI networks to genetic and genomic data.
The group is also interested in using other data modalities to study mental health, with projects investigating the potential of transcribed speech data to predict risk for psychotic disorders and mapping transcribed speech excerpts as networks.
Sarah’s research applies AI and data science approaches to better understand and predict brain development, cognition and mental health. To that end, she uses a range of methods from machine learning, network science and Natural Language Processing.
Related People
Benjamin Chidiac
University of Cambridge
PhD Student
Isaac Sebenius
Cambridge University
PhD Student
Sarah Morgan
Cambridge University
Departmental Early Career Academic Fellow, Accelerate Programme
Shrankhla Pandey
University of Cambridge
PhD student
Zhiyuan Song
University of Cambridge
PhD Student