Grant: Project Grant

Up to 30% of people with severe depression do not respond to standard therapies. They can benefit from brain stimulation, which is thought to modulate brain circuits. Stimulation is most commonly applied through the skull non-invasively with repetitive transcranial magnetic stimulation (rTMS) and with newer protocols like intermittent theta-burst stimulation (iTBS). Stimulation therapy has high personal and economic cost (e.g., significant time commitment from patients and caregivers, lost work hours, and an approximate $9,000 cost per treatment course). While these therapies benefit many patients, half do not respond, and patients require greater than 50% success rates given the personal and family burden. Our study will use brain imaging (MRI), electroencephalography (EEG), and whole brain computational modelling to identify and predict network changes in response to stimulation. Much of the existing knowledge in this field is based on average imaging features from many patients with depression. However, we know that the brain is not identical in everyone, and we expect the effects of depression to also vary among patients. In fact, we believe that these individual differences may explain why only about half of people respond to rTMS and iTBS. Our aims here are (1) to optimise the delivery of neurostimulation therapy with a precision medicine approach that uses brain modelling to best target unique anatomical and functional networks for each individual's depression; (2) to compare our personalised targeting predictions against previously developed group-based targeting predictions; and (3) to test the clinical effectiveness of our personalised strategy and see if it enhances treatment outcome. Our work has the potential to improve neurostimulation therapy by predicting how individual differences in brain function will change a person's response to this type of treatment.