Predicting recovery from stroke

I have been working with collaborators at the Wellcome Centre for Human Neuroimaging, particularly, Professor Cathy Price and her Ploras project, to improve current methods to predict recovery from stroke.

This has involved assessing problems of small samples and lack of sensitivity in this area,

Lorca-Puls, D. L., Gajardo-Vidal, A., White, J., Seghier, M. L., Leff, A. P., Green, D. W., ... Bowman, H. & Price, C. J. (2018). The impact of sample size on the reproducibility of voxel-based lesion-deficit mappings. Neuropsychologia, 115, 101-111.

Gajardo-Vidal, A., Lorca-Puls, D. L., Crinion, J. T., White, J., Seghier, M. L., Leff, A. P., ... & Price, C. J. (2018). How distributed processing produces false negatives in voxel-based lesion-deficit analyses. Neuropsychologia, 115, 124-133.

as well as countering the proportional recovery hypothesis of recovery from stroke,

Hope, T. M., Friston, K., Price, C. J., Leff, A. P., Rotshtein, P., & Bowman, H. (2019). Recovery after stroke: not so proportional after all? Brain.

Bonkhoff, A. K., Hope, T., Bzdok, D., Guggisberg, A. G., Hawe, R. L., Dukelow, S. P., ... & Bowman, H. (2020). Bringing proportional recovery into proportion: Bayesian modelling of post-stroke motor impairment. Brain, 143(7), 2189-2206.